Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to understand the underlying issues and difficulties that come about from missing data and their impact on subsequent analysis. There has been a great deal written on the theory developed for analyzing missing data for finite-dimensional parametric models. This includes an extensive literature on likelihood-based methods and multiple imputation. More recently, there has been increasing interest in semiparametric models which, roughly speaking, are models that include both a parametric and nonparametric component. Such models are popular because estimators in such models are more robust than in traditional parametric models. The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject. This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is at a level that is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible. Anastasios A. Tsiatis is the Drexel Professor of Statistics at North Carolina State University. His research has focused on developing statistical methods for the design and analysis of clinical trials, censored survival analysis, group sequential methods, surrogate markers, semiparametric methods with missing and censored data and causal inference and has been the major Ph.D. advisor for more than 30 students working in these areas. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the recipient of the Spiegelman Award and the Snedecor Award. He has been an Associate Editor of the Annals of Statistics and Statistics and Probability Letters and is currently an Associate Editor for Biometrika.

Parameter estimation. --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Stochastic systems --- Mathematical statistics. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Multivariate analysis --- Multiple imputation (Statistics) --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Imputation, Multiple (Statistics) --- Monte Carlo method --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This 2001 book presents a general theory as well as a constructive methodology to solve 'observation problems', that is, reconstructing the full information about a dynamical process on the basis of partial observed data. A general methodology to control processes on the basis of the observations is also developed. Illustrative but also practical applications in the chemical and petroleum industries are shown. This book is intended for use by scientists in the areas of automatic control, mathematics, chemical engineering and physics.

Observers (Control theory) --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Observability (Control theory) --- State estimator (Control theory) --- State observer (Control theory) --- Control theory

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Preface We are surrounded by missing data. Problems created by missing data in statistical analysis have long been swept under the carpet. These times are now slowly coming to an end. The array of techniques to deal with missing data has expanded considerably during the last decennia. This book is about one such method: multiple imputation. Multiple imputation is one of the great ideas in statistical science. The technique is simple, elegant and powerful. It is simple because it flls the holes in the data with plausible values. It is elegant because the uncertainty about the unknown data is coded in the data itself. And it is powerful because it can solve 'other' problems that are actually missing data problems in disguise. Over the last 20 years, I have applied multiple imputation in a wide variety of projects. I believe the time is ripe for multiple imputation to enter mainstream statistics. Computers and software are now potent enough to do the required calculations with little e ort. What is still missing is a book that explains the basic ideas, and that shows how these ideas can be put to practice. My hope is that this book can ll this gap. The text assumes familiarity with basic statistical concepts and multivariate methods. The book is intended for two audiences: - (bio)statisticians, epidemiologists and methodologists in the social and health sciences; - substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes. In writing this text, I have tried to avoid mathematical and technical details as far as possible. Formula's are accompanied by a verbal statement that explains the formula in layman terms.

Multivariate analysis --- Multiple imputation (Statistics) --- Missing observations (Statistics) --- 51-77 --- 519.23 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Imputation, Multiple (Statistics) --- Monte Carlo method --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Mathematics for behavioural and social sciences

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The inspiration to write this book came from many years of teaching about Mplus and answering questions on Mplus Discussion and Mplus support. It became clear that once people leave school, it is difficult to keep up with the newest methodology. The purpose of this book is to provide researchers with information that is not readily available to them and that we believe is important for their research. Many topics such as linear regression analysis; mediation analysis; causal inference; regression analysis with categorical, count, and censored outcomes; Bayesian analysis; and missing data analysis have entire books devoted to them. This book does not attempt to replace these books but rather to give useful and manageable summaries of these topics and show how the analyses are implemented in Mplus. The technical level is kept at a minimum but still requires an introductory statistics background and a good background in regression.

Quantitative methods in social research --- Analiza regresji. --- Statystyka. --- Regression analysis --- Mediation (Statistics) --- Bayesian statistical decision theory --- Missing observations (Statistics) --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mediation analysis (Statistics) --- Mediational analysis (Statistics) --- Statistical mediation --- Statistical mediation analysis --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Data processing --- Mplus. --- #SBIB:303H4 --- #SBIB:303H520 --- Informatica in de sociale wetenschappen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen