Distributed Solar: The Democratizaton of Energy
Category Archives: John Kruschke
“Bayesian replication analysis” (by John Kruschke)
“… the ability to express [hypotheses] as distributions over parameters …” Bayesian estimation supersedes the t-test: (Also by Professor Kruschke.)
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 dichotomising continuous variables, dichotomizing continuous variables, premature categorization, splines
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Dikran Marsupial’s excellent bit on hypothesis testing applied to climate, or how it should be applied, if at all
Frankly, I wish some geophysicists and climate scientists wrote more as if they thoroughly understood this, let alone deniers to try to discredit climate disruption. See “What does statistically significant actually mean?”. Of course, while statistical power of a test … Continue reading
