Semiparametric theory and missing data pdf

The book includes much material from reliability theory in engineering and survival analysis in medic The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains.

This book is the first to offer a systematic presentatio This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and gen This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models.

It presents an up-to-date account of theory and methods in analysis of these models as well as their applica For more than 40 years, SAGE has been one of the leading international publishers of works on quantitative research methods in the social sciences. This new collection provides readers with a representative sample of the best Reliability and survival analysis are important applications of stochastic mathematics probability, statistics and stochastic processes that are usually covered separately in spite of the similarity of the involved mathemat A practical guide to analysing partially observed data.

Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the The concepts of estimands, analyses estimators , and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing s This book is devoted to biased sampling problems also called choice-based sampling in Econometrics parlance and over-identified parameter estimation problems.

Biased sampling problems appear in many areas of research, inclu This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation.

It dis This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many meas Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials.

In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical i Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them.

The te Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal Cepal Review is the leading journal for the study of economic and social development issues in Latin America and the Caribbean.

Edited by the Economic Commission for Latin America, each issue focuses on economic trends, indus Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice. The book, A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error.

In particular, the data collection stage is a potential sourc The a Exponential smoothing methods have been around since the s, and are still the most popular forecasting methods used in business and industry.

However, a modeling framework incorporating stochastic models, likelihood calcu This paper investigates the impacts of sustainable land management SLM on water security and poverty based on an evaluation of a watershed level SLM program promoted in Amhara regional state of Ethiopia.

A household survey Provides a self-contained comprehensive treatment of both one-sample and K-sample goodness-of-fit methods by linking them to a common theory backbone Contains many data examples, including R-code and a specific R-package for This volume is the first book-length treatment of model-based geostatistics. The text is expository, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a Kenward,Anastasios Tsiatis,Geert Verbeke.

The application to missing data is also clearly of great interest. This comprehensive monograph offers an in-depth look at the associated theory ….

It was a great pleasure to read this masterful account of semiparametric theory for missing data problems …. It provides a valuable resource because it contains an up-to-date literature review and an exceptional account of state of the art research on the necessary theory. The exposition is instructive and rigorous enough. There are many important examples, oriented to biomedical applications. The monograph will be useful for graduate and post-graduate students in statistics and biostatistics, as well as researchers in statistics and survival analysis.

Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available. Semiparametric Theory and Missing Data. Authors view affiliations Anastasios A. Unifies the two approaches to the topic of missing data. Front Matter Pages i-xvi. Introduction to Semiparametric Models. Pages Hilbert Space for Random Vectors. The Geometry of Influence Functions. Semiparametric Models. Other Examples of Semiparametric Models. Models and Methods for Missing Data.

Missing and Coarsening at Random for Semiparametric Models. Approximate Methods for Gaining Efficiency.