Andere Kunden interessierten sich auch für
![Linear Mixed-Effects Models Using R]( "Linear Mixed-Effects Models Using R")
Linear Mixed-Effects Models Using R95,99 €![Linear and Generalized Linear Mixed Models and Their Applications]( "Linear and Generalized Linear Mixed Models and Their Applications")
Linear and Generalized Linear Mixed Models and Their Applications149,99 €![Mixed-Effects Models in S and S-PLUS]( "Mixed-Effects Models in S and S-PLUS")
Mixed-Effects Models in S and S-PLUS170,99 €![Mixed-Effects Models in S and S-PLUS]( "Mixed-Effects Models in S and S-PLUS")
Mixed-Effects Models in S and S-PLUS161,99 €![Uncertainty in Engineering]( "Uncertainty in Engineering")
Uncertainty in Engineering15,99 €![Efficient MCMC Estimation of Mixed Effects Binary Logit Models]( "Efficient MCMC Estimation of Mixed Effects Binary Logit Models")
Efficient MCMC Estimation of Mixed Effects Binary Logit Models27,99 €![Stein Estimation]( "Stein Estimation")
Stein Estimation33,99 €
This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.