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Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software.

Mathematical statistics --- Regression Analysis --- Nonparametric statistics --- Nonparametric statistics. --- Regression analysis. --- Regression analysis --- 519.233 --- 519.233 Parametric methods --- Parametric methods --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Mathematical Sciences --- Probability

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Mathematical statistics --- 519.2 --- Social sciences --- -#SBIB:303H10 --- #SBIB:303H520 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Probability. Mathematical statistics --- Statistical methods --- Methoden en technieken: algemene handboeken en reeksen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Regression analysis. --- Statistical methods. --- 519.2 Probability. Mathematical statistics --- Regression analysis --- #SBIB:303H10 --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Regression Analysis --- Social sciences - Statistical methods

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Mathematical statistics --- Regression analysis. --- Correlation (Statistics) --- Social sciences --- Analyse de régression --- Corrélation (Statistique) --- Sciences sociales --- Statistical methods. --- Méthodes statistiques --- Regression Analysis --- Statistical methods --- #SBIB:303H520 --- #SBIB:303H523 --- 519.2 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Methoden sociale wetenschappen: associatie, correlatie --- Probability. Mathematical statistics --- Methoden en technieken --- Statistiek --- statistiek --- Correlation (Statistics). --- Statistiek. --- statistiek. --- 519.2 Probability. Mathematical statistics --- Analyse de régression --- Corrélation (Statistique) --- Méthodes statistiques --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Least squares --- Probabilities --- Statistics --- Instrumental variables (Statistics) --- Graphic methods --- Social sciences - Statistical methods

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Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event – a parameter is either identified or not – and to view point identification as a pre-condition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski’s research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. Conservative nonparametric analysis enables researchers to learn from the available data without imposing untenable assumptions. It enables establishment of a domain of consensus among researchers who may hold disparate beliefs about what assumptions are appropriate. Charles F. Manski is Board of Trustees Professor at Northwestern University. He is author of Identification Problems in the Social Sciences and Analog Estimation Methods in Econometrics. He is a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, and the Econometric Society.

Distribution (Probability theory) --- Regression Analysis --- Stochastic processes --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Statistics. --- Econometrics. --- Statistical Theory and Methods. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law. --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Regression analysis. --- Mathematical statistics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistics for Social Sciences, Humanities, Law. --- Statistics . --- Economics, Mathematical --- Statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics