Aug 2nd, 2010| 05:21 pm | Posted by vlk

There is an ambitious project afoot to build a 3D map of a meteor stream during the Perseids on Aug 11-12. I got this missive about it from the organizer, Chris Crawford:

This will be one of the better years for Perseids; the moon, which often interferes with the Perseids, will not be a problem this year. So I’m putting together something that’s never been done before: a spatial analysis of the Perseid meteor stream. We’ve had plenty of temporal analyses, but nobody has ever been able to get data over a wide area — because observations have always been localized to single observers. But what if we had hundreds or thousands of people all over North America and Europe observing Perseids and somebody collected and collated all their observations? This is crowd-sourcing applied to meteor astronomy. I’ve been working for some time on putting together just such a scheme. I’ve got a cute little Java applet that you can use on your laptop to record the times of fall of meteors you see, the spherical trig for analyzing the geometry (oh my aching head!) and a statistical scheme that I *think* will reveal the spatial patterns we’re most likely to see — IF such patterns exist. I’ve also got some web pages describing the whole shebang. They start here:

http://www.erasmatazz.com/page78/page128/PerseidProject/PerseidProject.html

I think I’ve gotten all the technical, scientific, and mathematical problems solved, but there remains the big one: publicizing it. It won’t work unless I get hundreds of observers. That’s where you come in. I’m asking two things of you:

1. Any advice, criticism, or commentary on the project as presented in the web pages.

2. Publicizing it. If we can get that ol’ Web Magic going, we could get thousands of observers and end up with something truly remarkable. So, would you be willing to blog about this project on your blog?

3. I would be especially interested in your comments on the statistical technique I propose to use in analyzing the data. It is sketched out on the website here:

http://www.erasmatazz.com/page78/page128/PerseidProject/Statistics/Statistics.html

Given my primitive understanding of statistical analysis, I expect that your comments will be devastating, but if you’re willing to take the time to write them up, I’m certainly willing to grit my teeth and try hard to understand and implement them.

Thanks for any help you can find time to offer.

Chris Crawford

Tags:

Announcement,

Aug 2010,

August,

meteors,

modeling,

Perseids,

spatial-temporal modeling Category:

Astro,

Misc,

News,

Objects,

Stat |

Comment
Nov 21st, 2009| 05:06 am | Posted by hlee

by Emanuel Parzen in * Statistical Science* 2004, Vol 19(4), pp.652-662 JSTOR

I teach that statistics (done the quantile way) can be simultaneously frequentist and Bayesian, confidence intervals and credible intervals, parametric and nonparametric, continuous and discrete data. My first step in data modeling is identification of parametric models; if they do not fit, we provide nonparametric models for fitting and simulating the data. The practice of statistics, and the modeling (mining) of data, can be elegant and provide intellectual and sensual pleasure. Fitting distributions to data is an important industry in which statisticians are not yet vendors. We believe that unifications of statistical methods can enable us to advertise, “What is your question? Statisticians have answers!”

I couldn’t help liking this paragraph because of its bitter-sweetness. I hope you appreciate it as much as I did.

Oct 15th, 2009| 06:46 pm | Posted by hlee

Astronomers rely on scatter plots to illustrate correlations and trends among many pairs of variables more than any scientists^{[1]}. Pages of scatter plots with regression lines are often found from which the slope of regression line and errors bars are indicators of degrees of correlation. Sometimes, too many of such scatter plots makes me think that, overall, resources for drawing nice scatter plots and papers where those plots are printed are wasted. Why not just compute correlation coefficients and its error and publicize the processed data for computing correlations, not the full data, so that others can verify the computation results for the sake of validation? A couple of scatter plots are fine but when I see dozens of them, I lost my focus. This is another cultural difference. Continue reading ‘Scatter plots and ANCOVA’ »

Tags:

ANCOVA,

ANOVA,

approximation,

correlation,

Gaussianity,

graphics,

MADS,

modeling,

nonparametric,

parallel coordinates,

PCA,

quality,

quantity,

regression,

scatter plots Category:

arXiv,

Cross-Cultural,

Fitting,

Jargon,

Methods,

Stat,

Uncertainty |

Comment
Sep 1st, 2009| 07:43 pm | Posted by hlee

[arxiv:0906.3662] **The Statistical Analysis of fMRI Data** by 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’ »

Tags:

data aquisition,

experimental design,

fMRI,

ICA,

image analysis,

image processing,

localization,

modeling,

pipeline,

preprocessing,

similarities,

Spatial,

temporal,

time series,

voxel Category:

arXiv,

Cross-Cultural,

Data Processing,

Imaging,

Jargon,

Methods,

Stat |

Comment
Feb 9th, 2009| 06:02 am | Posted by hlee

When I was studying astronomy, during when I once became a subject for a social science survey study about life in a department where gender bias is extreme (I was only female), people often asked me how to forecast weather or how to predict future (boys often get questions related to becoming astronauts in addition to weather men and astrologists). Relating astronomy to earth science still happens. Statisticians that I met at conferences, often tried to associate my efforts on astronomical data with those of geologists and meteorologists, who often use stochastic models and spatial temporal models, dimensional extensions of models in time series. Because of this confusion between astronomy and meteorology/geology/oceanology, and the longer history of wide statistical applications found from the latter subjects (a good counter example is the *least square method* by Gauss but I cannot think more examples to contradict my statement that statistics is used widely among earth scientists with rich history), from time to time my attention has been paid to various applications and models in those subjects so as to find a thread for similar applications for astronomy. Although I don’t like the misconception of astronomy equal to meteorology or geoscience, those scientific fields, what so ever, share at least one commonality that statistical methods are applied to analyzing satellite data. Continue reading ‘[ArXiv] Special Issue from Annals of Applied Statistics’ »

Mar 30th, 2008| 11:16 pm | Posted by hlee

I began to study statistics with the notion that statistics is the study of information (retrieval) and a part of information is uncertainty which is taken for granted in our random world. Probably, it is the other way around; information is a part of uncertainty. Could this be the difference between Bayesian and frequentist?

__The statistician’s task is to articulate the scientist’s uncertainties in the language of probability, and then to compute with the numbers found__: cited from Continue reading ‘Statistics is the study of uncertainty’ »

Jan 18th, 2008| 02:24 pm | Posted by hlee

Seven preprints were chosen this week and two mentioned model selection. Continue reading ‘[ArXiv] 3rd week, Jan. 2008’ »

Tags:

AIC,

Bayesian,

BIC,

CLT,

correlation,

F-test,

FoF,

hypothesis testing,

Kolmogorov-Smirnoff test,

LRT,

Model Selection,

modeling,

sunspots Category:

arXiv |

Comment