Category Archives: generalized linear models

Senn’s `… never having to say you are certain’ guest post from Mayo’s blog

via S. Senn: Being a statistician means never having to say you are certain (Guest Post) See also: E. Cai’s blog post “Applied Statistics Lesson of the Day – The Matched Pairs Experimental Design”, from February 2014 A. Deaton, N. … Continue reading

Posted in abstraction, American Association for the Advancement of Science, American Statistical Association, cancer research, data science, ecology, experimental design, generalized linear mixed models, generalized linear models, Mathematics and Climate Research Network, medicine, sampling, statistics, the right to know | 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

Generating supports for classification rules in black box regression models

Inspired by the extensive and excellent work in approximate Bayesian computation (see also), especially that done by Professors Christian Robert and colleagues (see also), and Professor Simon Wood (see also), it occurred to me that the complaints regarding lack of … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, Bayesian inversion, generalized linear models, machine learning, numerical analysis, numerical software, probabilistic programming, rationality, reasonableness, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, support of black boxes | Leave a comment

All I do is complain, complain …

I was reviewing a presentation given as part of a short course in the machine learning genre today, and happened across the following two bullets, under the heading “Strictly Stationary Processes”: Predicting a time series is possible if and only … Continue reading

Posted in bifurcations, chaos, citizen science, convergent cross-mapping, dynamic linear models, dynamical systems, engineering, Floris Takens, generalized linear models, geophysics, George Sughihara, ignorance, Lenny Smith, Lorenz, mathematics, maths, meteorology, prediction, probability, rationality, reasonableness, statistics, stochastic algorithms, stochastic search, stochastics, Takens embedding theorem, the right to know, time series | 1 Comment

Is Earth Much More Sensitive to CO2 Than Thought?

Originally posted on Climate Denial Crock of the Week:
A nahcolite from the Eocene Green River Formation. Credit: Timothy Lowenstein Ancient climates on Earth may have been more sensitive to carbon dioxide than was previously thought, according to new…

Posted in Anthropocene, Carbon Cycle, carbon dioxide, climate, climate change, climate data, climate disruption, differential equations, diffusion processes, dynamic linear models, dynamical systems, environment, fossil fuels, generalized linear models, geophysics, global warming, Hyper Anthropocene, Principles of Planetary Climate, risk, science | Leave a comment

Southern Oscillation (SOI) correlated with Outgoing Longwave Radiation (OLR)

To the climate community this is nothing at all new, but I spotted these time series today and thought they would make a nice exhibit on how something people have direct control over, greenhouse gas emissions, affect a “teleconnection mechanism” … Continue reading

Posted in AMETSOC, bifurcations, carbon dioxide, climate, climate change, climate disruption, climate models, Dan Satterfield, differential equations, dynamic linear models, dynamical systems, ENSO, environment, forecasting, generalized linear models, geophysics, global warming, greenhouse gases, IPCC, Mathematica, mathematics, maths, meteorology, NCAR, NOAA, numerical software, oceanography, open data, physics, population biology, Principles of Planetary Climate, rationality, reasonableness, science, Spaceship Earth, state-space models, thermodynamics, time series | Leave a comment