Drawing from the authors' own work and from the most recent
developments in the field, Missing Data in Longitudinal Studies:
Strategies for Bayesian Modeling and Sensitivity Analysis describes
a comprehensive Bayesian approach for drawing inference from
incomplete data in longitudinal studies. To illustrate these
methods, the authors employ several data sets throughout that cover
a range of study designs, variable types, and missing data issues.
The book first reviews modern approaches to formulate and interpret
regression models for longitudinal data. It then discusses key
ideas in Bayesian inference, including specifying prior
distributions, computing posterior distribution, and assessing
model fit. The book carefully describes the assumptions needed to
make inferences about a full-data distribution from incompletely
observed data. For settings with ignorable dropout, it emphasizes
the importance of covariance models for inference about the mean
while for nonignorable dropout, the book studies a variety of
models in detail. It concludes with three case studies that
highlight important features of the Bayesian approach for handling
nonignorable missingness. With suggestions for further reading at
the end of most chapters as well as many applications to the health
sciences, this resource offers a unified Bayesian approach to
handle missing data in longitudinal studies.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
