Distributed Solar: The Democratizaton of Energy
Category Archives: probit regression
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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Earth Day, my hope
Posted in carbon dioxide, Carl Sagan, Charles Darwin, citizen science, citizenship, civilization, clean disruption, climate, climate change, climate education, compassion, conservation, Darwin Day, demand-side solutions, ecology, economics, education, efficiency, energy reduction, environment, ethics, forecasting, fossil fuel divestment, geophysics, history, humanism, investing, investment in wind and solar energy, IPCC, mathematics, maths, meteorology, NCAR, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, privacy, probit regression, R, rationality, Ray Pierrehumbert, reasonableness, reproducible research, risk, science, science education, scientific publishing, Scripps Institution of Oceanography, sociology, the right to know, Unitarian Universalism, UU Humanists, WHOI, wind power
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