Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

Examining meningitis mainly from a bacterial perspective, but also including an overview of viral, fungal and chronic meningitis, this book describes the anatomy of the meninges and clinical signs and symptoms of this disease. Individual organisms that cause meningitis worldwide are dealt with in specific chapters, describing in detail how these pathogens interact with the human host at both a molecular and cellular level. The book provides a thorough understanding of bacterial virulence factors, adhesion and invasion mechanisms, the nature of known host cell receptors and the host cell response.