Partial Identification of Probability Distributions:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XII, 179 p) |
| ISBN: | 9780387217864 9780387004549 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/b97478 |
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| 500 | |a 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. | ||
| 500 | |a 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 | ||
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Datensatz im Suchindex
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| discipline | Mathematik |
| doi_str_mv | 10.1007/b97478 |
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| isbn | 9780387217864 9780387004549 |
| issn | 0172-7397 |
| language | English |
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| spelling | Manski, Charles F. Verfasser aut Partial Identification of Probability Distributions by Charles F. Manski New York, NY Springer New York 2003 1 Online-Ressource (XII, 179 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 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 Statistics Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Parameteridentifikation (DE-588)4210689-8 gnd rswk-swf Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd rswk-swf Wahrscheinlichkeitsverteilung (DE-588)4121894-2 s Parameteridentifikation (DE-588)4210689-8 s 1\p DE-604 https://doi.org/10.1007/b97478 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Manski, Charles F. Partial Identification of Probability Distributions Statistics Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Parameteridentifikation (DE-588)4210689-8 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd |
| subject_GND | (DE-588)4210689-8 (DE-588)4121894-2 |
| title | Partial Identification of Probability Distributions |
| title_auth | Partial Identification of Probability Distributions |
| title_exact_search | Partial Identification of Probability Distributions |
| title_full | Partial Identification of Probability Distributions by Charles F. Manski |
| title_fullStr | Partial Identification of Probability Distributions by Charles F. Manski |
| title_full_unstemmed | Partial Identification of Probability Distributions by Charles F. Manski |
| title_short | Partial Identification of Probability Distributions |
| title_sort | partial identification of probability distributions |
| topic | Statistics Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Parameteridentifikation (DE-588)4210689-8 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd |
| topic_facet | Statistics Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Parameteridentifikation Wahrscheinlichkeitsverteilung |
| url | https://doi.org/10.1007/b97478 |
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