Category Archives: GLMMs

A quick note on modeling operational risk from count data

Posted on 11 September 2018 by ecoquant

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

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

Posted on 11 November 2017 by ecoquant

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 →

Posted in American Meteorological Association, American Statistical Association, AMETSOC, anomaly detection, climate, climate change, climate data, data science, evidence, experimental design, generalized linear mixed models, GISTEMP, GLMMs, global warming, model comparison, model-free forecasting, reblog, sampling, sampling networks | Leave a comment