£128.97

Chapman and Hall/CRC Analysis of Incomplete Multivariate Data

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Description

Statistical methods for handling missing data have advanced significantly over the last twenty years. While tools like the EM algorithm, multiple imputation, and Markov Chain Monte Carlo offer flexible ways to manage inference in large classes of missing-data problems, many analysts still struggle to apply these developments to their routine work. Analysis of Incomplete Multivariate Data helps bridge the gap between theoretical developments and practical application. This book makes modern missing-data tools accessible to a broad audience, moving beyond complex theory to provide usable insights for data analysts. It is an essential resource for those looking to improve how they handle incomplete datasets in professional and academic settings.

Key Features

Bridges the gap between complex statistical theory and everyday practical application for data analysts.

Covers essential modern methods including the EM algorithm and its various extensions.

Provides guidance on using multiple imputation for managing incomplete datasets effectively.

Explores Markov Chain Monte Carlo tools to provide reliable inference for missing-data problems.

Makes advanced statistical developments accessible to a wide and diverse professional audience.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 August 1997
Listed Since
08 February 2007

Barcode

No barcode data available

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