The AstroStat Slog

I often feel irksome whenever I see a function being normalized over a feasible parameter space and it being used as a probability density function (pdf) for further statistical inference. In order to be a suitable pdf, normalization has to be done over a measurable space not over a feasible space. Such practice often yields biased best fits (biased estimators) and improper error bars. On the other hand, validating a measurable space under physics seems complicated. To be precise, we often lost in translation. Continue reading ‘A short note on Probability for astronomers’ »

Because of blogging and projects I worked on, I happened to collect quite many publications in Astronomy. The collection is biased toward my personal interests. However, these authors discussed statistics in a wide range. So, I felt my astronomical bibliography can be useful to slog audience. Some areas could match your interests. Or your own name can be found. Continue reading ‘astronomy bibliography’ »

Please, IMS Bulletin, v.38 (10) check p.11 of this pdf file for the whole article. Continue reading ‘From Terence’s stuff: You want proof?’ »

When I begin to subscribe arXiv/astro-ph and arXiv/stat, although only for a year I listed astro-ph papers featuring relatively advanced statistics, I also kept more papers relevant to astrostatistics beyond astro-ph or introducing hot topics in statistics and computer science for astronomical data applications. While creating my own arXiv as follows, I had a hope to write up short introductions of statistics that are unlikely known to most of astronomers (like my MADS) and matching subjects/targets in astronomy. I thought such effort could spawn new collaborations or could expand understanding of statistics among astronomers (see Magic Crystal). Well, I couldn’t catch up the growth rate and it’s about time to terminate the hope. However, I thought some papers can be useful to some slog subscribers. I hope they do. Continue reading ‘arxiv list’ »

He was one of the frequently cited statisticians in this slog because of his influence in statistics. It is extremely difficult to avoid his textbooks and his establishment of theoretical statistics when one begins to comprehend and to appreciate the modern theoretical statistics. To me, Testing Statistical Hypotheses and Theory of Point Estimation are two pillars of graduate statistical education. In addition, Elements of Large Sample Theory and Nonparametrics: Statistical Methods Based on Ranks are also eye openers. Continue reading ‘Erich Lehmann’ »