## Regression: Models, Methods and ApplicationsThe aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference. |

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Regression: Models, Methods and Applications Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian Marx No preview available - 2013 |

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additive models algorithm analysis approach assume assumption asymptotic autocorrelation average B-splines basis functions Bayesian inference binary boldsymbol{\beta boosting Chap classical linear model cluster coefficient of determination computed confidence intervals considered construction continuous covariates correlation function corresponding covariance matrix defined density derived design matrix errors example explanatory variables Fahrmeir full conditional Gaussian given heteroscedastic homoscedastic interaction interpretation iterations Kneib knots LASSO least squares estimator likelihood linear model linear regression linear regression model living area log-likelihood logit model MCMC ML estimator model choice multivariate Munich rent index nonlinear effects nonparametric regression normal distribution observations obtain optimal outliers penalized least squares penalty Poisson posterior prior quantile quantile regression random effects random intercept model regression coefficients regression line regression models response variable ridge regression sample scatter plot shows smoothing parameter spatial effect specific standard Statistics studentized residuals Theorem univariate variance vec{\theta vector zero