From Quantile Probability and Statistical Data Modeling

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.

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