Partial identification of probability distributions
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The work Partial identification of probability distributions represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Partial identification of probability distributions
Resource Information
The work Partial identification of probability distributions represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Partial identification of probability distributions
 Statement of responsibility
 Charles F. Manski
 Subject

 Distribution (Probability theory)
 Electronic books
 MATHEMATICS  Probability & Statistics  General
 Regression analysis
 Regression analysis
 Statistical Theory and Methods
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law
 Distribution (Probability theory)
 Language
 eng
 Summary
 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 precondition 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 Manskis 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
 Cataloging source
 N$T
 Dewey number
 519.2/4
 Index
 index present
 LC call number
 QA273.6
 LC item number
 .M294 2003eb
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Springer series in statistics
Context
Context of Partial identification of probability distributionsWork of
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