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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

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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

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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

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"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

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Investment climate Surveys are valuable instruments that improve our understanding of the economic, social, political, and institutional factors determining economic growth, particularly in emerging and transition economies. However, at the same time, they have to overcome some difficult issues related to the quality of the information provided; measurement errors, outlier observations, and missing data that are frequently found in these datasets. This paper discusses the applicability of recent procedures to deal with missing observations in investment climate surveys. In particular, it presents a simple replacement mechanism - for application in models with a large number of explanatory variables - which in turn is a proxy of two methods: multiple imputations and an export-import algorithm. The performance of this method in the context of total factor productivity estimation in extended production functions is evaluated using investment climate surveys from four countries: India, South Africa, Tanzania, and Turkey. It is shown that the method is very robust and performs reasonably well even under different assumptions on the nature of the mechanism generating missing data.

Business environment --- E-Business --- Economic Theory & Research --- Enterprise surveys --- Equipment --- Imputation method --- Imputation process --- Information and Communication Technologies --- Information and Records Management --- Information Security & Privacy --- Innovation --- Intangible assets --- Macroeconomics and Economic Growth --- Manufacturing --- Marketing --- Missing data --- Missing value --- Missing values --- Multiple imputation --- Multiple imputations --- Private sector --- Private Sector Development --- Productivity --- Result --- Results --- Science and Technology Development --- Statistical & Mathematical Sciences --- Telecommunications --- Web