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Biased sampling, over-identified parameter problems and beyond
This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Econ...
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Lenguaje: | eng |
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Springer
2017
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Acceso en línea: | https://dx.doi.org/10.1007/978-981-10-4856-2 http://cds.cern.ch/record/2272831 |
_version_ | 1780954959546679296 |
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author | Qin, Jing |
author_facet | Qin, Jing |
author_sort | Qin, Jing |
collection | CERN |
description | This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. . |
id | cern-2272831 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
publisher | Springer |
record_format | invenio |
spelling | cern-22728312021-04-21T19:09:14Zdoi:10.1007/978-981-10-4856-2http://cds.cern.ch/record/2272831engQin, JingBiased sampling, over-identified parameter problems and beyondMathematical Physics and MathematicsThis book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. .Springeroai:cds.cern.ch:22728312017 |
spellingShingle | Mathematical Physics and Mathematics Qin, Jing Biased sampling, over-identified parameter problems and beyond |
title | Biased sampling, over-identified parameter problems and beyond |
title_full | Biased sampling, over-identified parameter problems and beyond |
title_fullStr | Biased sampling, over-identified parameter problems and beyond |
title_full_unstemmed | Biased sampling, over-identified parameter problems and beyond |
title_short | Biased sampling, over-identified parameter problems and beyond |
title_sort | biased sampling, over-identified parameter problems and beyond |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-981-10-4856-2 http://cds.cern.ch/record/2272831 |
work_keys_str_mv | AT qinjing biasedsamplingoveridentifiedparameterproblemsandbeyond |