Category Archives: maximum likelihood

Just because the data lies some times doesn’t mean it’s okay to censor it

Or, there’s no such thing as an outlier … Eli put up a post titled “The Data Lies. The Crisis in Observational Science and the Virtue of Strong Theory” at his lagomorph blog. Think of it: Data lying. Obviously this … Continue reading

Posted in Akaike Information Criterion, American Association for the Advancement of Science, American Meteorological Association, American Statistical Association, AMETSOC, Anthropocene, Bayes, Bayesian, climate, climate change, climate models, data science, dynamical systems, ecology, Eli Rabett, environment, Ethan Deyle, George Sughihara, Hao Ye, Hyper Anthropocene, information theoretic statistics, IPCC, Kalman filter, kriging, Lenny Smith, maximum likelihood, model comparison, model-free forecasting, physics, quantitative ecology, random walk processes, random walks, science, smart data, state-space models, statistics, Takens embedding theorem, the right to know, Timothy Lenton, Victor Brovkin | 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

Six cases of models

The previous post included an attempt to explain land surface temperatures as estimated by the BEST project using a dynamic linear model including regressions on both quarterly CO2 concentrations and ocean heat content. The idea was to check the explanatory … Continue reading

Posted in AMETSOC, anemic data, Anthropocene, astrophysics, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, climate, climate change, climate data, climate disruption, climate models, dlm package, dynamic linear models, dynamical systems, environment, fossil fuels, geophysics, Giovanni Petris, global warming, greenhouse gases, Hyper Anthropocene, information theoretic statistics, maths, maximum likelihood, meteorology, model comparison, numerical software, Patrizia Campagnoli, Rauch-Tung-Striebel, Sonia Petrone, state-space models, stochastic algorithms, stochastic search, SVD, time series | 1 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