Analysis of Incomplete Multivariate Data

Name: Analysis of Incomplete Multivariate Data
Brand: CRC Press

CRC Press

The author focuses on applications, as necessary, to help readers thoroughly understand the statistical properties of the methods and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, complemented by extended discussions and practical advice. > The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis.Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets