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.

How would you assign orders to multivariate data? If you have your strategy to achieve this ordering task, I’d like to ask, “is your strategy affine invariant?” meaning that shift and rotation invariant. Continue reading ‘[MADS] data depth’ »

The breakdown point of the mean is asymptotically zero whereas the breakdown point of the median is 1/2. The breakdown point is a measure of the robustness of the estimator and its value reaches up to 1/2. In the presence of outliers, the mean cannot be a good measure of the central location of the data distribution whereas the median is likely to locate the center. Common plug-in estimators like mean and root mean square error may not provide best fits and uncertainties because of this zero breakdown point of the mean. The efficiency of the mean estimator does not guarantee its unbiasedness; therefore, a bit of care is needed prior to plugging in the data into these estimators to get the best fit and uncertainty. There was a preprint from [arXiv] about the use of median last week. Continue reading ‘[ArXiv] use of the median’ »