Category Archives: Akaike Information Criterion

`Environmental science in a post-truth world’ (Lubchenco and Kammen)

Jane Lubchenco is a Professor at Oregon State University, and was administrator of the U.S. NOAA from 2009 through 2013, the U.S. Science Envoy for the Ocean at the State Department from 2014 to 2016, and the president of the … Continue reading

Posted in Akaike Information Criterion, American Association for the Advancement of Science, American Statistical Association, being carbon dioxide, Buckminster Fuller, climate, climate change, coastal communities, coasts, ecological services, ecology, environment, environmental law, evidence, global warming, Humans have a lot to answer for, Hyper Anthropocene, ignorance, Jane Lubchenco, marine biology, mass extinctions, population biology, population dynamics, quantitative biology, quantitative ecology, risk, science, Spaceship Earth, sustainability, T'kun Olam, temporal myopia, the tragedy of our present civilization | Leave a comment

Why smooth?

I’ve encountered a number of blog posts this week which seem not to understand the Bias-Variance Tradeoff in regard to Mean-Squared-Error. These arose in connection with smoothing splines, which I was studying in connection with multivariate adaptive regression splines, that … Continue reading

Posted in Akaike Information Criterion, American Statistical Association, Antarctica, carbon dioxide, climate change, denial, global warming, information theoretic statistics, likelihood-free, multivariate adaptive regression splines, non-parametric model, science denier, smoothing, splines, statistical dependence | 1 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

Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)

(Updated 2016-05-08, to provide reference for plateaus of ML functions in vicinity of MLE.) Simpson’s Paradox is one of those phenomena of data which really give Statistics a substance and a role, beyond the roles it inherits from, say, theoretical … Continue reading

Posted in Akaike Information Criterion, approximate Bayesian computation, Bayes, Bayesian, evidence, Frequentist, games of chance, information theoretic statistics, Kalman filter, likelihood-free, mathematics, maths, maximum likelihood, Monte Carlo Statistical Methods, probabilistic programming, rationality, Rauch-Tung-Striebel, Simpson's Paradox, state-space models, statistical dependence, statistics, stochastics | Leave a comment