The AstroStat Slog

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 have been observing some sorts of misconception about statistics and statistical nomenclature evolution in astronomy, which I believe, are attributed to the lack of references in the astronomical society. There are some textbooks designed for junior/senior science and engineering students, which are likely unknown to astronomers. Example-wise, these books are not suitable, to my knowledge. Although I never expect astronomers to learn standard graduate (mathematical) statistics textbooks, I do wish astronomers go beyond Numerical Recipes (W. H. Press, S. A. Teukolsky, W. T. Vetterling, & B. P. Flannery) and Error Data Reduction and Analysis for the Physical Sciences (P. R. Bevington & D. K. Robinson). Here are some good ones written by astronomers, engineers, and statisticians: Continue reading ‘Books – a boring title’ »

From arxiv/astro-ph:0707.1982v1,
Nflation: observable predictions from the random matrix mass spectrum by Kim and Liddle

To my knowledge, random matrix received statisticians’ interests fairly recently and SAMSI (Statistical and Applied Mathematical Sciences Institute) offered a semester long program on High Dimensional Inference and Random Matrices (tutorials and lecture notes can be found) during Fall 2006 . However, my knowledge is very limited to make a comment or critic on Kim and Liddle’s paper. Clearly, nonetheless, this paper is not about random matrix theory but about its straightforward application to the cosmological model viability.
Continue reading ‘[ArXiv] Random Matrix, July 13, 2007’ »