The AstroStat Slog

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.

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’ »

1. 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[↩]

[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’ »

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’ »

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’ »

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