#### SINGS

From SINGS (Spitzer Infrared Nearby Galaxies Survey): Isn’t it a beautiful Hubble tuning fork? Continue reading ‘SINGS’ »

Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders

Author Archive

From SINGS (Spitzer Infrared Nearby Galaxies Survey): Isn’t it a beautiful Hubble tuning fork? Continue reading ‘SINGS’ »

When it comes to applying statistics for measuring goodness-of-fit, the Pearson χ^{2} test is the dominant player in a race and the Kolmogorov-Smirnoff test statistic trails far behind. Although it seems almost invisible in this race, there are more various non-parametric statistics for testing goodness-of-fit and for comparing the sampling distribution to a reference distribution as legitimate race participants trained by many statisticians. Listing their names probably useful to some astronomers when they find the underlying assumptions for the χ^{2} test do not match the data. Perhaps, some astronomers want to try other nonparametric test statistics other than the K-S test. I’ve seen other test statistics in astronomical journals from time to time. Depending on data and statistical properties, one test statistic could work better than the other; therefore, it’s worthwhile to keep the variety in one’s mind that there are other tests beyond the χ^{2} test goodness-of-fit test statistic. Continue reading ‘Goodness-of-fit tests’ »

I decide to discuss **Kalman Filter** a while ago for the slog after finding out that this popular methodology is rather underrepresented in astronomy. However, it is not completely missing from ADS. I see that the fulltext search and all bibliographic source search shows more results. Their use of **Kalman filter,** though, looked similar to the usage of “genetic algorithms” or “Bayes theorem.” Probably, the broad notion of **Kalman filter** makes it difficult my finding **Kalman Filter** applications by its name in astronomy since often wheels are reinvented (algorithms under different names have the same objective). Continue reading ‘[MADS] Kalman Filter’ »

So far, I didn’t complain much related to my “*statistician learning astronomy*” experience. Instead, I’ve been trying to emphasize how fascinating it is. I hope that more statisticians can join this adventure when statisticians’ insights are on demand more than ever. However, this positivity seems not working so far. In two years of this slog’s life, there’s no posting by a statistician, except one about BEHR. Statisticians are busy and well distracted by other fields with more tangible data sets. Or compared to other fields, too many obstacles and too high barriers exist in astronomy for statisticians to participate. I’d like to talk about these challenges from my ends.^{[1]} Continue reading ‘data analysis system and its documentation’ »

- This is quite an overdue posting. Links and associated content can be outdated.[↩]

By accident, a piece of paper was found from my old text book. I have no idea who wrote this, nor how old it is. Too old to be obsolete? But it has general description to become a good person and scientist Continue reading ‘To Become a Good Astronomer’ »

Thanks to a Korean solar physicist^{[1]} I was able to gather the following websites and some relevant information on Space Weather Forecast in action, not limited to literature nor toy data.

- Space Weather Research Lab at NJIT
- SEEDS — Solar Eruptive Event Detection System at George Mason University.
- CACTUS A software package for ‘Computer Aided CME Tracking
- SRON in the Netherlands

Continue reading ‘More on Space Weather’ »

- I must acknowledge him for his kindness and patience. He was my wikipedia to questions while I was studying the Sun.[↩]

A number of practical Bayesian data analysis books are available these days. Here, I’d like to introduce two that were relatively recently published. I like the fact that they are rather technical than theoretical. They have practical examples close to be related with astronomical data. They have R codes so that one can try algorithms on the fly instead of jamming probability theories. Continue reading ‘[Books] Bayesian Computations’ »

Soon it’ll not be qualified for [MADS] because I saw some abstracts with the phrase, **compressed sensing** from arxiv.org. Nonetheless, there’s one publication within refereed articles from ADS, so far.

http://adsabs.harvard.edu/abs/2009MNRAS.395.1733W.

Title:Compressed sensing imaging techniques for radio interferometry

Authors:Wiaux, Y. et al. Continue reading ‘[MADS] compressed sensing’ »

I happened to observe a surge of principle component analysis (PCA) and independent component analysis (ICA) applications in astronomy. The PCA and ICA is used for separating mixed components with some assumptions. For the PCA, the decomposition happens by the assumption that original sources are orthogonal (uncorrelated) and mixed observations are approximated by multivariate normal distribution. For ICA, the assumptions is sources are independent and not gaussian (it grants one source component to be gaussian, though). Such assumptions allow to set dissimilarity measures and algorithms work toward maximize them. Continue reading ‘[ArXiv] component separation methods’ »

ARCH (**autoregressive conditional heteroscedasticity**) is a statistical model that considers *the variance of the current error term to be a function of the variances of the previous time periods’ error terms*. I heard that this model made Prof. Engle a Nobel prize recipient. Continue reading ‘[MADS] ARCH’ »

[arxiv:0906.3662]

The Statistical Analysis of fMRI Databy Martin A. Lindquist

Statistical Science, Vol. 23(4), pp. 439-464

This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.

**List of similarities** Continue reading ‘[ArXiv] Statistical Analysis of fMRI Data’ »

**Kriging** is the first thing that one learns from a spatial statistics course. If an astronomer sees its definition and application, almost every astronomer will say, “Oh, I know this! It is like the 2pt correlation function!!” At least this was my first impression when I first met **kriging.**

There are three distinctive subjects in spatial statistics: **geostatistics**, **lattice data analysis**, and **spatial point pattern analysis.** Because of the resemblance between the spatial distribution of observations in coordinates and the notion of spatially random points, spatial statistics in astronomy has leaned more toward the spatial point pattern analysis than the other subjects. In other fields from immunology to forestry to geology whose data are associated spatial coordinates of underlying geometric structures or whose data were sampled from lattices, observations depend on these spatial structures and scientists enjoy various applications from geostatistics and lattice data analysis. Particularly, **kriging** is the fundamental notion in **geostatistics** whose application is found many fields. Continue reading ‘[MADS] Kriging’ »

Over the few years, at the heart of astronomical researches, I see astronomers treat statistics like a magic crystal. Continue reading ‘Magic Crystal’ »

Statistical Resampling Methods are rather unfamiliar among astronomers. Bootstrapping can be an exception but I felt like it’s still unrepresented. Seeing an recent review paper on **cross validation** from [arXiv] which describes basic notions in theoretical statistics, I couldn’t resist mentioning it here. **Cross validation** has been used in various statistical fields such as classification, density estimation, model selection, regression, to name a few. Continue reading ‘[ArXiv] Cross Validation’ »

I’ve heard many times about ** the lack of documentation** of this extensive data analysis system, **ciao**. I saw people still using ciao 3.4 although the new version 4 has been available for many months. Although **ciao** is not the only tool for Chandra data analysis, it was specifically designed for it. Therefore, I expect it being used frequently with popularity. But the reality is against my expectation. Whatever (fierce) discussion I’ve heard, it has been irrelevant to me because **ciao** is not intended for statistical analysis. Then, out of sudden, after many months, a realization hit me. **ciao is different from other data analysis systems and softwares.** This difference has been a hampering factor of introducing ciao outside the Chandra scientist community and of gaining popularity. This difference was the reason I often got lost in finding suitable documentations. Continue reading ‘Where is ciao X ?’ »