I’ve noticed that there are rapidly growing interests and attentions in data mining and machine learning among astronomers but the level of execution is yet rudimentary or partial because there has been no comprehensive tutorial style literature or book for them. I recently introduced a machine learning book written by an engineer. Although it’s a very good book, it didn’t convey the foundation of machine learning built by statisticians. In the quest of searching another good book so as to satisfy the astronomers’ pursuit of (machine) learning methodology with the proper amount of statistical theories, the first great book came along is **The Elements of Statistical Learning**. It was chosen for this writing not only because of its fame and its famous authors (Hastie, Tibshirani, and Friedman) but because of my personal story. In addition, the 2nd edition, which contains most up-to-date and state-of-the-art information, was released recently.

First, the book website:

The Elements of Statistical Learning byHastie, Tibshirani, and Friedman

You’ll find examples, R codes, relevant publications, and plots used in the text books.

Second, I want to tell how I learned about this book before its first edition was published. Everyone has a small moment of meeting very famous people. Mine is shaking hands with President Clinton, in 2000. I still remember the moment vividly because I really wanted to tell him that ice cream was dripping on his nice suit but the top of the line guards blocked my attempt of speaking/pointing icecream dripping with a finger afterward the hand shaking. No matter what context is, shaking hands with one of the greatest presidents is a memorable thing. Yet it was not my cherishing moment because of icecreaming dripping and scary bodyguards. My most cherishing moment of meeting famous people is the half an hour conversation with late Prof. Leo Breinman (click for my two postings about him), author of probability textbook, creator of CART, and the most forefront pioneer in machine learning.

The conclusion of that conversation was a book soon to be published after explaining him my ideas of applying statistics to astronomical data and his advices to each problems. I was not capable to understand every statistics so that his answer about this new coming book at that time was the most relevant and apt one.

This conversation happened during the 3rd Statistical Challenges in Modern Astronomy (SCMA). Not long passed since I began my graduate study in statistics but had an opportunity to assist the conference organizer, my advisor Dr. Babu and to do some chores during the conference. By accident, I read the book by Murtagh about multivariate data analysis, so I wanted to speak to him. Except that, I have no desire to speak renown speakers and attendees. Frankly, I didn’t have any idea who’s who at the conference and a few years later, I realized that the conference dragged many famous people and the density of such people was higher than any conference I attended. Who would have imagine that I could have a personal conversation with Prof. Breiman, at that time. I have seen enough that many famous professors train people during conferences. Getting a chance for chatting some seconds are really hard and tall/strong people push someone small like me away always.

The story goes like this: a sunny perfect early summer afternoon, he was taking a break for a cigar and I finished my errands for the session. Not much to do until the end of session, I decided to take some fresh air and I spotted him enjoying his cigar. Only the worst was that I didn’t know he was the person of CART and the founder of statistical machine learning. Only from his talk from the previous session, I learned he was a statistician, who did data mining on galaxies. So, I asked him if I can join him and ask some questions related to some ideas that I have. One topic I wanted to talk about classification of SN light curves, by that time from astronomical text books, there are Type I & II, and Type I has subcategories, Ia, Ib, and Ic. Later, I heard that there is Type III. But the challenge is observations didn’t happen with equal intervals. There were more data mining topics and the conversation went a while. In the end, he recommended me a book which will be published soon.

Having such a story, a privilege of talking to late Prof. Breiman through an very unique meeting, SCMA, before knowing the fame of the book, this book became one of my favorites. The book, indeed, become popular, around that time, almost only book discussing statistical learning; therefore, it was an excellent textbook for introducing statistics to engineerers and machine learning to statisticians. In the mean time, statistical learning enjoyed popularity in many disciplines that have data sets and urging for learning with the aid of machine. Now books and journals on machine learning, data mining, and knowledge discovery (KDD) became prosperous. I was so delighted to see the 2nd edition in the market to bridge the gap over the years.

I thank him for sharing his cigar time, probably his short free but precious time for contemplation, with me. I thank his patience of spending time with such an ignorant girl with a foreign english accent. And I thank him for introducing a book which will became a bible in the statistical learning community within a couple of years (I felt proud of myself that I access the book before people know about it). Perhaps, astronomers cannot have many joys from this book that I experienced from how I encounter the book, who introduced the book, whether the book was used in a course, how often book is referred, etc. But I assure that it’ll narrow the gap in the notions how astronomers think about data mining (preprocessing, pipelining, and bulding catalogs) and how statisticians treat data mining. The newly released 2nd edition would help narrowing the gap further and assist astronomers to coin brilliant learning algorithms specific for astronomical data. [The END]

—————————– Here, I patch my scribbles about the book.

What distinguish this book from other machine learning books is that not only authors are big figures in statistics but also fundamentals of statistics and probability are discussed in all chapters. Most of machine learning books only introduce elementary statistics and probability in chapter 2, and no basics in statistics is discussed in later chapters. Generally, empirical procedures, computer algorithms, and their results without presenting basic theories in statistics are presented.

You might want to check the book’s website for data sets if you want to try some ideas described there

The Elements of Statistical Learning

In addition to its historical footprint in the field of statistical learning, I’m sure that some astronomers want to check out topics in the book. It’ll help to replace some data analysis methods in astronomy celebrating their centennials sooner or later with state of the art methods to cope with modern data.

This new edition reflects some evolutions in statistical learning whereas the first edition has been an excellent harbinger of the field. Pages quoted from the 2nd edition.

[p.28] Suppose in fact that our data arose from a statistical model $Y=f(X)+e$ where the random error e has E(e)=0 and is independent of X. Note that for this model, f(x)=E(Y|X=x) and in fact the conditional distribution Pr(Y|X) depends on X

onlythrough the conditional mean f(x).

The additive error model is a useful approximation to the truth. For most systems the input-output pairs (X,Y) will not have deterministic relationship Y=f(X). Generally there will be other unmeasured variables that also contribute to Y, including measurement error. The additive model assumes that we can capture all these departures from a deterministic relationship via the error e.

How statisticians envision “model” and “measurement errors” quite different from astronomers’ “model” and “measurement errors” although in terms of “additive error model” they are matching due to the properties of Gaussian/normal distribution. Still, the dilemma of hen or eggs exists prior to any statistical analysis.

[p.30] Although somewhat less glamorous than the learning paradigm, treating supervised learning as a problem in function approximation encourages the geometrical concepts of Euclidean spaces and mathematical concepts of probabilistic inference to be applied to the problem. This is the approach taken in this book.

Strongly recommend to read chapter 3, Linear Methods for Regression: In astronomy, there are so many important coefficients from regression models, from Hubble constant to absorption correction (temperature and magnitude conversion is another example. It seems that these relations can be only explained via OLS (ordinary least square) with the homogeneous error assumption. Yet, books on regressions and linear models are not generally thin. As much diversity exists in datasets, more amount of methodology, theory and assumption exists in order to reflect that diversity. One might like to study the statistical properties of these indicators based on mixture and hierarchical modeling. Some inference, say population proportion can be drawn to verify some hypotheses in cosmology in an indirect way. Understanding what regression analysis and assumptions and how statistician efforts made these methods more robust and interpretable, and reflecting reality would change forcing E(Y|X)=aX+b models onto data showing correlations (not causality).

]]>(via /.)

]]>When I was teaching statistics, despite undergraduate courses, there were both undergraduate and graduate students of various fields except astrophysics majors. I wondered why they were not encouraged to take some basic statistics whereas they were encouraged to take some computer science courses. As there are many astronomers good at programming and designing tools, I’m sure that recommending students to take statistics courses will renovate astronomical data analysis procedures (beyond Bevington’s book) and hind theories (statistics and mathematics per se, not physics laws).

Here’s an interesting lecture for developing a curriculum for the new era in computer science and why the basic probability theory and statistics is important to raise versatile computer scientists. It could be a bit out dated now because I saw it several months ago.

About a little more than the half way through the lecture, he emphasizes that probability course partaking the computer science curriculum. I wonder any astronomy professor has similar arguments and stresses for any needs of basic probability theories to be learned among young future astrophysicists in order to prevent many statistics misuses appearing in astronomical literature. Particularly confusions between fitting (estimating) and inference (both model assessment and uncertainty quantification) are frequently observed in literature where authors claim their superior statistics and statistical data analysis. I personally sometimes attribute this confusion to the lack of distinction between what is random and what is deterministic, or strong believe in their observed and processed data absent from errors and probabilistic nature.

Many introductory books introduce very interesting problems many of which have some historical origins to introduce probability theories (many anecdotes). One can check out the very basics, **probability axioms**, ** and ****measurable function** from wikipedia. With examples, probability is high school or lower level math that you already know but with jargon you’ll like to recite lexicons many times so that you are get used to foundations, basics, and their theories.

We often mention **measurable** to discuss random variables, uncertainties, and distributions without verbosity. “Assume **measurable space** … ” saves multiple paragraphs in an article and changes the structure of writing. This short adjective implies so many assumptions depending on statistical models and equations that you are using for best fits and error bars.

Consider a LF, that is truncated due to observational limits. The common practice I saw is drawing a histogram in a way that the adaptive binning makes the overall shape reflecting a partial bell shape curve. Thanks to its smoothed look, scientists impose a gaussian curve to partially observed data and find parameter estimates that determine the shape of this gaussian curve. There is no imputation step to fake unobserved points to comprise the full probability space. The parameter space of gaussian curves frequently does not coincide with the physically feasible space; however, such discrepancy is rarely discussed in astronomical literature and subsequent biased results look like a taboo.

Although astronomers emphasize the importance of uncertainties, factorization nor stratification of uncertainties has never been clear (model uncertainty, systematic uncertainty or bias, statistical uncertainties or variance). Hierarchical relationships or correlations among these different uncertainties are never addressed in a full measure. Basics of probability theory and the understanding of random variables would help to characterize uncertainties both in mathematical sense and astrophysical sense. This knowledge also assist appropriate quantification of these characterized uncertainties.

Statistical models are rather simple compared to models of astrophysics. However, statistics is the science of understanding uncertainties and randomness and therefore, some strategies of transcribing from complicated astrophysical models into statistical models, in order to reflect the probabilistic nature of observed (or parameters, for Bayesian modeling), are necessary. Both raw or processed data manifest the behavior of random variables. Their underlying processes determine not only physics models but also statistical models written in terms of random variables and the link functions connecting physics and uncertainties. To my best understanding, bridging and inventing statistical models for astrophysics researches seem tough due to the lack of awareness of basics of probability theory.

Once I had a chance to observe a Decadal survey meeting, which covered so diverse areas in astronomy. They discussed new projects, advancing current projects, career developments, and a little bit about educating professional astronomers apart from public reach (which often receives more importance than university curriculum. I also believe that wide spread public awareness of astronomy is very important). What I missed while I observing the meeting is that interdisciplinary knowledge transferring efforts to broaden the field of astronomy and astrophysics nor curriculum design ideas. Because of its long history, I thought astronomy is a science of everything. Marching a path for a long time made astronomy more or less the most isolated and exclusive science.

Perhaps asking astronomy majors taking multiple statistics courses is too burdensome; therefore being taught by faculty who are specialized in (statistical) data analysis organizes a data analysis course and incorporates several hours of basic probability is more realistic and what I anticipate. With a few hours of bringing fundamental notions in random variables and probability, the claims of “statistical rigorous methods and powerful results” will become more appropriate. Currently, statistics is science but in astronomy literature, it looks more or less like an adjective that modify methods and results like “powerful”, “superior”, “excellent”, “better”, “useful,” and so on. Basics of probability is easily incorporated into introduction of algorithms in designing experiments and optimization methods, which are currently used in a brute force fashion^{[1]}.

Occasionally, I see gems from arxiv written by astronomers. Their expertise in astronomy and their interest in statistics has produced intriguing accounts for statistically rigorous data analysis and inference procedures. Their papers includes explanation of fundamentals of statistics and probability more appropriate to astronomers than statistics textbooks for scientists and engineers of different fields. I wish more astronomers join this venture knowing basics and diversities of statistics to rectify many unconscious misuses of statistics while they argue that their choice of statistics is the most powerful one thanks to plausible results.

- What I mean by a brute force fashion is that trying all methods listed in the software manual, and then later, stating that the method A gave most plausible values that matches with data in a scatter plot

It is widely believed that under some fairly general conditions, MLEs are consistent, asymptotically normal, and efficient. Stephen Stigler has elegantly documented some of Fisher’s troubles when he wanted a proof. You want proof? Of course you can pile on assumptions so that the proof is easy.

If checking your assumptions in any particular case is harder than checking the conclusion in that case, you will have joined a great tradition.

I used to think that efficiency was a thing for the theorists (I can live with inefficiency), that normality was a thing of the past (we can simulate), but that—in spite of Ralph Waldo Emerson—consistency is a thing we should demand of any statistical procedure. Not any more. These days we can simulate in and around the conditions of our data, and learn whether a novel procedure behaves as it should in that context. If it does, we might just believe the results of its application to our data. Other people’s data? That’s their simulation, their part of the parameter space, their problem. Maybe some theorist will take up the challenge, and study the procedure, and produce something useful. But if we’re still waiting for that with MLEs in general (canonical exponential families are in good shape), I wouldn’t hold my breath for this novel procedure. By the time a few people have tried the new procedure, each time checking its suitability by simulation in their context, we will have built up a proof by simulation. Shocking? Of course.

Some time into my career as a statistician, I noticed that I don’t check the conditions of a theorem before I use some model or method with a set of data. I think in statistics we need derivations, not proofs. That is, lines of reasoning from some assumptions to a formula, or a procedure, which may or may not have certain properties in a given context, but which, all going well, might provide some insight. The evidence that this might be the case can be mathematical, not necessarily with epsilon-delta rigour, simulation, or just verbal. Call this “a statistician’s proof ”. This is what I do these days. Should I be kicked out of the IMS?

After reading many astronomy literature, I develop a notion that astronomers like to use the maximum likelihood as a robust alternative to the chi-square minimization for fitting astrophysical models with parameters. I’m not sure it is truly robust because not many astronomy paper list assumptions and conditions for their MLEs.

Often I got confused with their target parameters. They are not parameters in statistical models. They are not necessarily satisfy the properties of probability theory. I often fail to find statistical properties of these parameters for the estimation. It is rare checking statistical modeling procedures with assumptions described by Prof. Speed. Even derivation is a bit short to be called “rigorous statistical analysis.” (At least I wish to see a sentence that “It is trivial to derive the estimator with this and that properties”).

Common phrases I confronted from astronomical literature is that authors’ strategy is **statistically rigorous, superior, or powerful** without showing why and how it is rigorous, superior, or powerful. I tried to convey these pitfalls and general restrictions in their employed statistical methods. Their strategy is not “statistically robust” nor “statistically powerful” nor “statistically rigorous.” Statisticians have own measures of “superiority” to discuss the improvement in their statistics, analysis strategies, and methodology.

It has not been easy since I never intend to case specific fault picking every time I see these statements. A method believed to be robust can be proven as not a robust method with your data and models. By simulations and derivations with the sufficient description of conditions, your excellent method can be presented with statistical rigors.

Within similar circumstances for statistical modeling and data analysis, there’s a trade off between robustness and conditions among statistical methodologies. Before stating a particular method adopted is robust or rigid, powerful or insensitive, efficient or inefficient, and so on; derivation, proof, or simulation studies are anticipated to be named the analysis and procedure is statistically excellent.

Before it gets too long, I’d like say that statistics have traditions for declaring working methods via proofs, simulations, or derivations. Each has their foundations: assumptions and conditions to be stated as “robust”, “efficient”, “powerful”, or “consistent.” When new statistics are introduced in astronomical literature, I hope to see some additional effort of matching statistical conditions to the properties of target data and some statistical rigor (derivations or simulations) prior to saying they are “robust”, “powerful”, or “superior.”

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**Pilla, Loader, Volume-of-Tube Formula: Perturbation tests, mixture models, and scan statistics** - [0503033] (astro-ph) Battye, Moss
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**Applications of Bayes Model Selection to cosmological parameters** - [0304301] (astro-ph) Nakamichi, Morikawa, AIC, is galaxy dist’n non-extensive and non-gaussian?
- [0110230] (astro-ph) Nichol, Chong, Connolly, et al
- [0806.1506] (astro-ph) Unzicker, Fischer,
**2D galaxy dist’n, SDSS** - [0304005] (astro-ph) Maller, McIntosh, et al. (Angular correlation funtion, Power spectrum)
- [0108439] (astro-ph) Boschan (angular and 3D correlation functions)
- [9601103 (astro-ph) Maddox, Efstathiou, Sutherland (sys errors, angular correlation function)
- [0806.0520] Vio, Andreani
- [0807.4672] Zhang, Johnson, Little, Cao
- [0911.4546] Hobert, Roy, Robert
- [0911.4207] Calsaverini, Vicente (information theory and copula)
- [0911.4021] Fan, Wu, Feng (Local Quasi-Likelihood with a parametric guide) *
- [0911.4076] Hall, Jin, Miller
- [0911.4080] Genovese, Jin, Wasserman
- [0802.2174] Faure, Kneib, et al. (strong lense, COSMOS)
- [0802.1213] Bridle et al (Great08 Challenge)
- [0711.0690] Davies, Kovac, Meise (Nonparametric Regression, Confidence regions and regularization)
- [0901.3245] Nadler
- [0908.2901] Hong, Meeker, McCalley
- [0501221] (math) Cadre (Kernel Estimation of Density Level Sets)
- [0908.2926] Oreshkin, Coates (Error Propagation in Particle Filters)
- [0811.1663] *Lyons*
**(Open Statistical Issues in Particle Physics)** - [0901.4392] Johnstone, Lu (Sparse Principle Component Analysis)
- [0803.2095] Hall, Jin (HC)
- [0709.4078] Giffin (… Life after Shannon)
- [0802.3364] Leeb (model selection and evalutioin)
- [0810.4752] Luxburg, Scholkopf (Stat. Learning Theory…)
- [0708.1441] van de Weygaert, Schaap, The cosmic web: geometric analysis
- [0804.2752] Buhlmann, Hothorn (Boosting algorithms…)
- [0810.0944] Aydin, Pataki, Wang, Bullitt, Marron (PCA for trees)
- [0711.0989] Chen (SDSS, volume limited sample)
- [0709.1538] Einbeck, Evers, Bailer-Jones (Localized PC)
- [0610835] (math.ST) Lehmann (On LRTs)
- [0604410] (math.ST) Buntine, Discrete Component Analysis
- [0707.4621] Hallin, Paindaveine (semiparametrically efficient rank-based inference I)
- [0708.0079] Hallin, H. Oja, Paindaveine ( same as above II)
- [0708.0976] Singh, Xie, Strawderman (confidence distribution)
- [0706.3014] Gordon, Trotta (Bayesian calibrated significance levels.. the usage of p-values looks awkward)
- [0709.0711] Quireza, Rocha-Pinto, Maciel
- [0709.1208] Kuin, Rosen (measurement erros)
- [0709.1359] Huertas-Company, et al (SVM, morphological classification)
- [0708.2340] Miller, Kitching, Heymans, et. al. (Bayesian Galaxy Shape Measurement, weak lensing survey)
- [0709.4316] Farchione, Kabaila (confidence intervals for the normal mean)
- [0710.4245] Fearnhead, Papaspiliopoulos, Roberts (Particle Filters)
- [0705.4199] (astro-ph) Leccardi, Molendi , an unbiased temp estimator for stat. poor X-ray specra (can be improved… )
- [0712.1663] Meinshausen, *Bickel, Rice* (efficient blind search)
- [0706.4108] *Bickel, Kleijn, Rice* (Detecting Periodicity in Photon Arrival Times)
- [0704.1584] Leeb, Potscher (estimate the unconditional distribution of post model selection estimator)
- [0711.2509] Pope, Szapudi (Shrinkage Est. Power Spectrum Covariance matrix)
- [0703746] (math.ST) Flegal, Maran, Jones (MCMC: can we trush the third significant figure?)
- [0710.1965] (physics.soc-ph) Volchenkov, Blanchard, Sestieri of Venice
- [0712.0637] Becker, Silvestri, Owen, Ivezic, Lupton (in pursuit of LSST science requirements)
- [0703040] Johnston, Teodoro, *Martin Hendry* Completeness I: revisted, reviewed, and revived
- [0910.5449] Friedenberg, Genovese (multiple testing, remote sensing, LSST)
- [0903.0474] Nordman,
**Stationary Bootstrap’s Variance**(Check Lahiri99) - [0706.1062] (physics.data-an) Clauset, Shalizi, Newman (power law distributions in empirical data)
- [0805.2946] Kelly, Fan, Vestergaard (LF, Gaussian mixture, MCMC)
- [0503373] (astro-ph) Starck, Pires, Refregier (weak lensing mass reconstruction using wavelets)
- [0909.0349] Panaretos
- [0903.5463] Stadler, Buhlmann
- [0906.2128] Hall, Lee, Park, Paul
- [0906.2530] Donoho, Tanner
- [0905.3217] Hirakawa, Wolfe
- [0903.0464] Clarke, Hall
- [0701196] (math) Lee, Meng
- [0805.4136] Genovese, Freeman, Wasserman, NIchol, Miller
- [0705.2774] Kelly
- [0910.1473] Lieshout
- [0906.1698] Spokoiny
- [0704.3704] Feroz, Hobson
- [0711.2349] Muller, Welsh
- [0711.3236] Kabaila, Giri
- [0711.1917] Leng
- [0802.0536] Wang
- [0801.4627] Potscher, Scheider
- [0711.0660] Potscher, Leeb
- [0711.1036] Potscher
- [0702781] (math.st) Potscher
- [0711.0993] Kabaila, Giri
- [0802.0069] Ghosal, Lember, Vaart
- [0704.1466] Leeb, Potscher
- [0701781] (math) Grochenig, Potscher, Rauhut
- [0702703] (math.ST) Leeb, Potscher
- [astro-ph:0911.1777] Computing the Bayesian Evidence from a Markov Chain Monte Carlo Simulation of the Posterior Distribution (Martin Weinberg)

- [0812.4933] Wiaux, Jacques (Compressed sensing, interferometry)
- [0708.2184] Sung, Geyer
- [0811.1705] Meyer
- [0811.1700] Witten, Tibshirani
- [0706.1703] Land, SLosar
- [0712.1458] Loh, Zhu
- [0808.4042] Commenges
- [0806.3978] Vincent Vu, Bin Yu, Robert Kass
- [0808.4032] Stigler
- [0805.1944] astro-ph
- [0807.1815] Cabella, Marinucci
- [0808.0777] Buja, Kunsch
- [0809.1024] Xu, Grunwald
- [0807.4081] Roquain, Wiel
- [0806.4105] Rofling, Ribshirani
- [0808.0657 HUbert, Rousseeuw, Aelst
- [0112467] (astro-ph) Petrosian
- [0808.2902] Robert, Casella, A History of MCMC
- [0809.2754] Grunwald, VItanyi, Algorithmic INofmration THeory
- [0809.4866] Carter, Raich, Hero, An information geometric framework for DImensionality reduction
- [0809.5032] Allman, Matias, Rhodes
- [0811.0528] Owen
- [0811.0757] Chamandy, Tayler, Gosselin
- [0810.3985] Stute, Wang
- [0804.2996] Stigler
- [0807.4086] Commenges, Sayyareh, Letenneur…
- [0710.5343] Peng, Paul, MLE, functional PC, sparse longitudinal data
- [0709.1648] Cator, Jongbloed, et al. *Asymptotics: Particles, Processes, and Inverse problems*
- [0710.3478] *Hall, Qiu, Nonparametric Est. of a PSF in Multivariate Problems*
- [0804.3034] Catalan, Isern, Carcia-Berro, Ribas (some stellar clusters, LF, Mass F, weighted least square)
- [0801.1081] Hernandez, Valls-Gabaud, estimation of basic parameters, stellar populations
- [0410072] (math.ST) Donoho, Jin, HC, detecting sparse heterogeneous mixtures
- [0803.3863] Efron
- [0706.4190] Rondonotti, Marron, Park, SiZer for time series
- [0709.0709] Lian, Bayes and empirical Bayes changepoint problems
- [0802.3916] Carvalho, Rocha, Hobson, PowellSnakes
- [0709.0300] Roger, Ferrera, Lahav, et al, Decoding the spectra of SDSS early-type galaxies
- [0810.4807] Pesquet, et al. SURE, Signal/Image Devonvolution
- [0906.0346] (cs.DM) Semiparametric estimation of a noise model with quantization errors
- [0207026] (hep-ex) Barlow,
**Systematic Errors: Facts and Fictions** - [0705.4199, Leccardi, Molendi,
**unbiased temperature estimator for statistically poor x-ray spectra** - [0709.1208] Kuin, Rosen,
**measurement error**Swift - [0708.4316] Farchione, *Kabila*
**confidence intervals**for the normal mean utilizing prior information - [0708.0976] Singh, Xia, Strawderman
**confidence distribution** - [0901.0721] Albrecht, et al. (dark energy)
- [0908.3593] Singh, Scott, Nowak, adaptive hausdorff estimation of density level sets
- [0702052] (astro-ph) de Wit, Auchere, Multipectral analysis, sun, EUV, morphology
- [0706.1580] Lopes,
**photometric redshifts, SDSS** - [0106038] (astro-ph) Richards et al
**photometric redshifts of quasars**

There are quite many websites dedicated to python as you already know. Some of them talk only to astronomers. A tiny fraction of those websites are for statisticians but I haven’t met any statistician preferring only python. We take the gist of various languages. So, I’ll leave a general website aggregation, such as AstroPy (I think this website is extremely useful for astronomers), to enrich your bookmark under the “python” tab regardless of your profession. Instead, I’ll discuss some python libraries and modules that can be useful for those exercising astrostatistics and make their work easier. I must say that by intention I omitted a few modules because I was not sure their publicity and copyright sensitivity. If you have modules that can be introduced publicly, let me know. I’ll be happy to add them. If my description is improper and want them to be taken off, also let me know.

Over the past few years, python became the most common and versatile script language for both communities, and therefore, I believe, it would accelerate many collaborations. Much of my time is spent to find out how to read, maneuver, and handle raw data/image. Most of tactics for astronomers are quite unfamiliar, sometimes insensible to me (see my read.table() and data analysis system and its documentation). Somehow, one script language, thanks to its open and free intention to all communities, is promising by narrowing the gap for prosperous and efficient collaborations, **Python**

The first posting on this slog was about __Python.__ I thought that kicking off with a computer language relatively new and open to many communities could motivate me and others for more interdisciplinary works with diversity. After a few years, unfortunately, I didn’t achieve that goal. Yet, I still think that these libraries and modules, introduced below, to be useful for your transition from some programming languages, or for writing your own but pro bono wrapper for better communication with the others.

I’ll take numpy, scipy, and RPy for granted. For the plotting purpose, matplotlib seems most common.

**Reading astronomical data** (click links to download libraries, modules, and tutorials)

- First, start with Using Python for Interactive Data Analysis (in pdf) Quite useful manual, particularly for IDL users. It compares pros and cons of Python and IDL.
- IDLsave Simply, without IDL, a .save file becomes legible. This is a brilliant small module.
- PyRAF (I was really frustrated with IRAF and spent many sleepless nights. Apart from data reduction, I don’t remember much of statistics from IRAF except simple statistics for Gaussian populations. I guess PyRAF does better job). And there’s PyFITS for handling fits format data.
- APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format (this introduction is copied from the APLpy site).

**Statistics, Mathematics, or data science**

Due to RPy, introducing smaller modules seems not much worthy but quite many modules and library for statistics are available, not relying on R.

- MDP (Modular toolkit for Data Processing)

Multivariate data analysis methods like PCA, ICA, FA, etc. become very popular in the astronomical society. - pywavelets (Not only FT, various transformation methodologies are often used and wavelet transformation ranks top).
- PyIMSL (see my post, PyIMSL)
- PyMC I introduced this module in a century ago. It may be lack of versatility or robustness due to parametric distribution objects but I liked the tutorial very much from which one can expand and devise their own working MCMC algorithm.
- PyBUGS (I introduced this python wrapper in BUGS but the link to PyBUGS is not working anymore. I hope it revives.)
- SAGE (Software for Algebra and Geometry Experimentation) is a free open-source mathematics software system licensed under the GPL (Link to the online tutorial).
- python_statlib descriptive statistics for the python programming language.
- PYSTAT Nice website but the product is not available yet. Be aware! It is not PhyStat!!!

**Module for AstroStatistics**

**import inference** (Unfortunately, the links to examples and tutorial are not available currently)

Without clear objectives, it is not easy to pick up a new language. If you are used to work with one from alphabet soup, you most likely adhere to your choice. Changing alphabets or transferring language names only happens when your instructor specifically ask you to use their preferring languages and when analysis {modules, libraries, tools} are only available within that preferred language. Somehow, thanks to the object oriented style, python makes transition and communication easier than other languages. Furthermore, script languages are more intuitive and better interpretable.

]]>My astronomer neighbor mentioned this book a while ago and quite later I found intriguing quotes.

GIGO:Garbage in; garbage out. Fancy statistical methods will not rescue garbage data.Course notes of Raymond J. Carroll (2001)

I often see a statement like *data were grouped/binned to improve statistics.* This seems hardly true unless the astronomer knows the true underlying population distribution from which those realizations (either binned or unbinned) are randomly drawn. Nonetheless, smoothing/binning (modifying sample) can help hypothesis testing to infer the population distribution. This validation step is often ignored, though. For the righteous procedures of statistics application, I hope astronomers adopt the concepts in the design of experiments to collect good quality data without wasting resources. What I mean by wasting resources is that, due to the instrumental and atmospheric limitations, indefinite exposure is not necessary to collect good quality image. Instead of human eye inspection, machine can do the job. I guess that minimax type optimal points exist for operating telescopes, feature extraction/detection, or model/data quality assessment. Clarifying the sources of uncertainty and stratifying them for testing, sampling, and modeling purposes as done in analysis of variance is quite unexplored in astronomy. Instead, more efforts go to salvaging garbage and so far, many gems are harvested by tremendous amount of efforts. But, I’m afraid that it could get as much painful as gold miners’ experience during the mid 19th century gold rush.

Interval Estimates(p.51)

A common error is to specify a confidence interval in the form (estimate – k*standard error, estimate+k*standard error). This form is applicable only when an interval estimate is desired for the mean of a normally distributed random variable. Even then k should be determined for tables of the Student’s t-distribution and not from tables of the normal distribution.

How to get appropriate degrees of freedom seems most relevant to avoid this error when estimates are the coefficients of complex curves or equation/model itself. The t-distribution with a large d.f. (>30) is hardly discernible from the z-distribution.

Desirable estimators are impartial,consistent, efficient, robust, and minimum loss. Interval estimates are to be preferred to point estimates; they are less open to challenge for they convey information about the estimate’s precision.

Every Statistical Procedure Relies on Certain Assumptionsfor correctness.

What I often fail to find from astronomy literature are these assumptions. Statistics is not elixir to every problem but works only on certain conditions.

Know your objectives in testing. Know your data’s origins. Know the assumptions you feel comfortable with. Never assign probabilities to the true state of nature, but only to the validity of your own predictions. Collecting more and better data may be your best alternative

Unfortunately, the last sentence is not an option for astronomers.

From

Guidelines for a Meta-Analysis

Kepler was able to formulate his laws only because (1) Tycho Brahe has made over 30 years of precise (for the time) astronomical observations and (2) Kepler married Brahe’s daughter and thus gained access to his data.

Not exactly same but it reflects some reality of contemporary. Without gaining access to data, there’s not much one can do and collecting data is very painstaking and time consuming.

From

Estimating Coefficient

…Finally, if the error terms come from a distribution that is far from Gaussian, a distribution that is truncated, flattened or asymmetric, the p-values and precision estimates produced by the software may be far from correct.

Please, double check numbers from your software.

To quote Green and Silverman (1994, p. 50), “There are two aims in curve estimation, which to some extent conflict with one another, to maximize goodness-of-fit and to minimize roughness.

Statistically significant findings should serve as a motivation for further corroborative and collateral research rather than as a basis for conclusions.

To be avoided are a recent spate of proprietary algorithms available solely in software form that guarantee to find a best-fitting solution. In the worlds of John von Neumann, “With four parameters I can fit an elephant and with five I can make him wiggle his trunk.” Goodness of fit is no guarantee of predictive success, …

If physics implies wiggles, then there’s nothing wrong with an extra parameter. But it is possible that best fit parameters including these wiggles might not be the ultimate answer to astronomers’ exploration. It can be just a bias due to introducing this additional parameter for wiggles in the model. Various statistical tests are available and caution is needed before reporting best fit parameter values (estimates) and their error bars.

]]>First of all, I’d like to ask how you would like to estimate the chance of having nukes in a country? What this 80% implies here? But, before getting to the question, I’d like to discuss computing the chance of e coli infection, first.

From the frequentists perspective, computing the chance of e coli infection is investigating sample of lettuce and counts species that are infected: n is the number of infected species and N is the total sample size. 1% means one among 100. Such percentage reports and their uncertainties are very familiar scene during any election periods for everyone. From Bayesian perspective, Pr(p|D) ~ L(D|p) pi(p), properly choosing likelihoods and priors, one can estimate the chance of e coli infection and uncertainty. Understanding of sample species and a prior knowledge helps to determine likelihoods and priors.

How about the chance that country A has nukes? Do we have replicates of country A so that a committee investigate each country and count ones with nukes to compute the chance? We cannot do that. Traditional frequentist approach, based on counting, does not work here to compute the chance. Either using **fiducial likelihood** approach or **Bayesian ** approach, i.e. carefully choosing a likelihood function adequately (priors are only for Bayesian) allows one to compuate such probability of interest. In other words, those computed chances highly depend on the choice of model and are very subjective.

So, here’s my concern. It seems like that astronomers want to know the chance of their spectral data being described by a model (A*B+C)*D (each letter stands for one of models such as listed in Sherpa Models). This is more like computing the chance of having nukes in country A, not counting frequencies of the event occurrence. On the other hand, p-value from goodness of fit tests, LRTs, or F-tests is a number from the traditional frequentists’ counting approach. In other words, p-value accounts for, under the null hypothesis (the (A*B+C)*D model is the right choice so that residuals are Gaussian), how many times one will observe the event (say, reduced chi^2 >1.2) if the experiments are done N times. The problem is that we only have one time experiment and that one spectrum to verify the (A*B+C)*D is true. Goodness of fit or LRT only tells the goodness or the badness of the model, not the statistically and objectively quantified chance.

In order to know the chance of the model (A*B+C)*D, like A has nuke with p%, one should not rely on p-values. If you have multiple models, one could compute pairwise relative chances i.e. odds ratios, or Bayes factors. However this does not provide the uncertainty of the chance (astronomers have the tendency of reporting uncertainties of any point estimates even if the procedure is statistically meaningless and that quantified uncertainty is not statistical uncertainty, as in using delta chi^2=1 to report 68% confidence intervals). There are various model selection criteria that cater various conditions embedded in data to make a right model choice among other candidate models. In addition, post-inference for astronomical models is yet a very difficult problem.

In order to report the righteous chance of (A*B+C)*D requires more elaborated statistical modeling, always brings some fierce discussions between frequentists and Bayesian because of priors and likelihoods. Although it can be very boring process, I want astronomers to leave the problem to statisticians instead of using inappropriate test statistics and making creative interpretation of statistics.

Please, keep this question in your mind when you report probability: what kind of chance are you computing? The chance of e coli infection? Or the chance that A has nukes? Make sure to understand that p-values from data analysis packages does not tell you that the chance the model (A*B+C)*D is (one minus p-value)%. You don’t want to report one minus p-value from a chi-square test statistic as the chance that A has nukes.

]]>
[arXiv:stat.ME:0910.2585]

Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications

by *Murphy, Dean, and Raftery*

Classifying or clustering (or semi supervised learning) spectra is a very challenging problem from collecting statistical-analysis-ready data to reducing the dimensionality without sacrificing complex information in each spectrum. Not only how to estimate spiky (not differentiable) curves via statistically well defined procedures of estimating equations but also how to transform data that match the regularity conditions in statistics is challenging.

Another reason that astrophysics spectroscopic data classification and clustering is more difficult is that observed lines, and their intensities and FWHMs on top of continuum are related to atomic database and latent variables/hyper parameters (distance, rotation, absorption, column density, temperature, metalicity, types, system properties, etc). Frequently it becomes very challenging mixture problem to separate lines and to separate lines from continuum (boundary and identifiability issues). These complexity only appears in astronomy spectroscopic data because we only get indirect or uncontrolled data ruled by physics, as opposed to the the meat species spectra in the paper. These spectroscopic data outside astronomy are rather smooth, observed in controlled wavelength range, and no worries for correcting recession/radial velocity/red shift/extinction/lensing/etc.

Although the most relevant part to astronomers, i.e. spectroscopic data processing is not discussed in this paper, the most important part, statistical learning application to complex curves, spectral data, is well described. Some astronomers with appropriate data would like to try the variable selection strategy and to check out the classification methods in statistics. If it works out, it might save space for storing spectral data and time to collect high resolution spectra. Please, keep in mind that it is not necessary to use the same variable selection strategy. Astronomers can create better working versions for classification and clustering purpose, like Hardness Ratios, often used to reduce the dimensionality of spectral data since low total count spectra are not informative in the full energy (wavelength) range. Curse of dimensionality!.

]]>When having many pairs of variables that demands numerous scatter plots, one possibility is using parallel coordinates and a matrix of correlation coefficients. If Gaussian distribution is assumed, which seems to be almost all cases, particularly when parametrizing measurement errors or fitting models of physics, then error bars of these coefficients also can be reported in a matrix form. If one considers more complex relationships with multiple tiers of data sets, then one might want to check ANCOVA (ANalysis of COVAriance) to find out how statisticians structure observations and their uncertainties into a model to extract useful information.

I’m not saying those simple examples from wikipedia, wikiversity, or publicly available tutorials on ANCOVA are directly applicable to statistical modeling for astronomical data. Most likely not. Astrophysics generally handles complicated nonlinear models of physics. However, identifying dependent variables, independent variables, latent variables, covariates, response variables, predictors, to name some jargon in statistical model, and defining their relationships in a rather comprehensive way as used in ANCOVA, instead of pairing variables for scatter plots, would help to quantify relationships appropriately and to remove artificial correlations. Those spurious correlations appear frequently because of data projection. For example, datum points on a circle on the XY plane of the 3D space centered at zero, when seen horizontally, look like that they form a bar, not a circle, producing a perfect correlation.

As a matter of fact, astronomers are aware of removing these unnecessary correlations via some corrections. For example, fitting a straight line or a 2nd order polynomial for extinction correction. However, I rarely satisfy with such linear shifts of data with uncertainty because of changes in the uncertainty structure. Consider what happens when subtracting background leading negative values, a unrealistic consequence. Unless probabilistically guaranteed, linear operation requires lots of care. We do not know whether residuals y-E(Y|X=x) are perfectly normal only if μ and σs in the gaussian density function can be operated linearly (about Gaussian distribution, please see the post why Gaussianity? and the reference therein). An alternative to the subtraction is linear approximation or nonparametric model fitting as we saw through applications of principle component analysis (PCA). PCA is used for whitening and approximating nonlinear functional data (curves and images). Taking the sources of uncertainty and their hierarchical structure properly is not an easy problem both astronomically and statistically. Nevertheless, identifying properties of the observed both from physics and statistics and putting into a comprehensive and structured model could help to find out the causality^{[2]} and the significance of correlation, better than throwing numerous scatter plots with lines from simple regression analysis.

In order to understand why statisticians studied ANCOVA or, in general, **ANOVA (ANalysis Of VAriance)** in addition to the material in wiki:ANCOVA, you might want to check this page^{[3]} and to utilize your search engine with keywords of interest on top of ANCOVA to narrow down results.

From the linear model perspective, if a response is considered to be a function of redshift (z), then z becomes a covariate. The significance of this covariate in addition to other factors in the model, can be tested later when one fully fit/analyze the statistical model. If one wants to design a model, say rotation speed (indicator of dark matter occupation) as a function of redshift, the degrees of spirality, and the number of companions – this is a very hypothetical proposal due to my lack of knowledge in observational cosmology. I only want to point that the model fitting problem can be seen from statistical modeling like ANCOVA by identifying covariates and relationships – because the covariate z is continuous, and the degrees are fixed effect (0 to 7, 8 tuples), and the number of companions are random effect (cluster size is random), the comprehensive model could be described by ANCOVA. To my knowledge, scatter plots and simple linear regression are marginalizing all additional contributing factors and information which can be the main contributors of correlations, although it may seem Y and X are highly correlated in the scatter plot. At some points, we must marginalize over unknowns. Nonetheless, we still have some nuisance parameters and latent variables that can be factored into the model, different from ignoring them, to obtain advanced insights and knowledge from observations in many measures/dimensions.

Something, I think, can be done with a small/ergonomic chart/table via hypothesis testing, multivariate regression, model selection, variable selection, dimension reduction, projection pursuit, or names of some state of the art statistical methods, is done in astronomy with numerous scatter plots, with colors, symbols, and lines to account all possible relationships within pairs whose correlation can be artificial. I also feel that trees, electricity, or efforts can be saved from producing nice looking scatter plots. Fitting/Analyzing more comprehensive models put into a statistical fashion helps to identify independent variables or covariates causing strong correlation, to find collinear variables, and to drop redundant or uncorrelated predictors. Bayes factors or p-values can be assessed for comparing models, testing significance their variables, and computing error bars appropriately, not the way that the null hypothesis probability is interpreted.

Lastly, ANCOVA is a complete **[MADS]**.

- This is not an assuring absolute statement but a personal impression after reading articles of various fields in addition to astronomy. My readings of other fields tell that many rely on correlation statistics but less scatter plots by adding straight lines going through data sets for the purpose of imposing relationships within variable pairs
- the way that chi-square fitting is done and the goodness-of-fit test is carried out is understood by the notion that X causes Y and by the practice that the objective function, the sum of (Y-E[Y|X])^2/σ^2 is minimized
- It’s a website of Vassar college, that had a first female faculty in astronomy, Maria Mitchell. It is said that the first building constructed is the Vassar College Observatory, which is now a national historic landmark. This historical factor is the only reason of pointing this website to drag some astronomers attentions beyond statistics.