Distributed Solar: The Democratizaton of Energy
Blogroll
- AP Statistics: Sampling, by Michael Porinchak
- "The Expert"
- Dollars per BBL: Energy in Transition
- "Talking Politics" podcast
- Brian McGill's Dynamic Ecology blog
- Leadership lessons from Lao Tzu
- Beautiful Weeds of New York City
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Harvard's Project Implicit
- Gabriel's staircase
climate change
- Professor Robert Strom's compendium of resources on climate change
- "Getting to the Energy Future We Want," Dr Steven Chu
- Équiterre
- Ray Pierrehumbert's site related to "Principles of Planetary Climate"
- Sir David King
- Solar Gardens Community Power
- Social Cost of Carbon
- Paul Beckwith
- "Climate science is setttled enough"
- Jacobson WWS literature index
Archives
Jan Galkowski
Category Archives: GLMMs
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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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
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
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ecoquant
