### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Ives and Dakos techniques for regime changes in series
- American Association for the Advancement of Science (AAAS)
- distributed solar and matching location to need
- All about models
- Mrooijer's Numbers R 4Us
- South Shore Recycling Cooperative
- Healthy Home Healthy Planet
- The Mermaid's Tale
- Los Alamos Center for Bayesian Methods
- Survey Methodology, Prof Ron Fricker

### climate change

- Dessler's 6 minute Greenhouse Effect video
- Exxon-Mobil statement on UNFCCC COP21
- "Climate science is setttled enough"
- Berkeley Earth Surface Temperature
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- James Powell on sampling the climate consensus
- Climate impacts on retail and supply chains
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- CLIMATE ADAM

### Archives

### Jan Galkowski

# Category Archives: Generalize Additive 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 dichotomising continuous variables, dichotomizing continuous variables, premature categorization, splines
Leave a comment

## On Smart Data

One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading

Posted in Akaike Information Criterion, Bayes, Bayesian, Bayesian inversion, big data, bigmemory package for R, changepoint detection, data science, data streams, dlm package, dynamic generalized linear models, dynamic linear models, dynamical systems, Generalize Additive Models, generalized linear models, information theoretic statistics, Kalman filter, linear algebra, logistic regression, machine learning, Markov Chain Monte Carlo, mathematics, mathematics education, maths, maximum likelihood, MCMC, Monte Carlo Statistical Methods, multivariate statistics, numerical analysis, numerical software, numerics, quantitative biology, quantitative ecology, rationality, reasonableness, sampling, smart data, state-space models, statistical dependence, statistics, the right to know, time series
Leave a comment

## Gavin Simpson updates his temperature analysis

See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.

Posted in AMETSOC, Anthropocene, astrophysics, Berkeley Earth Surface Temperature project, carbon dioxide, changepoint detection, climate, climate change, climate data, climate disruption, climate models, ecology, environment, evidence, Gavin Simpson, Generalize Additive Models, geophysics, global warming, HadCRUT4, hiatus, Hyper Anthropocene, information theoretic statistics, Kalman filter, maths, meteorology, numerical analysis, R, rationality, reasonableness, splines, time series
Leave a comment