Advanced Data Analysis from an Elementary Point of View

Pittsburgh,USA: Carnegie Mellon University. - 586 p.
Страничка автора
"Продвинутый анализ данных с элементарной точки зрения".
Конспект лекций по методам статистического анализа, читаемых аспирантам и студентам старших курсов проф. Космо Шализи в университете Карнеги-Меллон.
Серьезные проблемы обсуждаются легко и непринужденно, с обилием примеров и коротких скриптов на языке среды R. Мог бы претендовать на приз "Лучший учебник по углубленному курсу прикладной статистики".Model evaluation: statistical inference, prediction, and scientific inference; in-sample and out-of-sample errors, generalization and over-fitting, cross-validation; evaluating by simulating; the bootstrap; penalized fitting; mis-specification checks
Yet More Linear Regression: what is regression, really?; what ordinary linear regression actually does; what it cannot do; extensions
Smoothing: kernel smoothing, including local polynomial regression; splines; additive models; kernel density estimation
Generalized linear and additive models: logistic regression; generalized linear models; generalized additive models.
Latent variables and structured data: principal components; factor analysis and latent variables; latent cluster/mixture models; graphical models in general
Causality: graphical causal models; identification of causal effects from observations; estimation of causal effects; discovering causal structure
Dependent data: Markov models for time series without latent variables; hidden Markov models for time series with latent variables; longitudinal, spatial and network data