Category Archives: generalized linear mixed models

A quick note on modeling operational risk from count data

The blog statcompute recently featured a proposal encouraging the use of ordinal models for difficult risk regressions involving count data. This is actually a second installment of a two-part post on this problem, the first dealing with flexibility in count … Continue reading

Posted in American Statistical Association, Bayesian, Bayesian computational methods, count data regression, dichotomising continuous variables, dynamic generalized linear models, Frank Harrell, Frequentist, Generalize Additive Models, generalized linear mixed models, generalized linear models, GLMMs, GLMs, John Kruschke, maximum likelihood, model comparison, Monte Carlo Statistical Methods, multivariate statistics, nonlinear, numerical software, numerics, premature categorization, probit regression, statistical regression, statistics | Tagged , , , | Leave a comment

Senn’s `… never having to say you are certain’ guest post from Mayo’s blog

via S. Senn: Being a statistician means never having to say you are certain (Guest Post) See also: E. Cai’s blog post “Applied Statistics Lesson of the Day – The Matched Pairs Experimental Design”, from February 2014 A. Deaton, N. … Continue reading

Posted in abstraction, American Association for the Advancement of Science, American Statistical Association, cancer research, data science, ecology, experimental design, generalized linear mixed models, generalized linear models, Mathematics and Climate Research Network, medicine, sampling, statistics, the right to know | Leave a comment

Eli on “Tom [Karl]’s trick and experimental design“

A very fine post at Eli’s blog for students of statistics, meteorology, and climate (like myself) titled: Tom’s trick and experimental design Excerpt: This and the graph from Menne at the top shows that Karl’s trick is working. Although we … Continue reading

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