LEADER 03932nam  22006015  450 
001 9910254276103321
005 20200702161212.0
010   $a981-10-4856-8
024 7 $a10.1007/978-981-10-4856-2
035   $a(CKB)3710000001406284
035   $a(DE-He213)978-981-10-4856-2
035   $a(MiAaPQ)EBC4878121
035   $a(PPN)202988996
035   $a(EXLCZ)993710000001406284
100   $a20170615d2017      u|             0
101 0 $aeng
135   $aurnn#|||mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBiased Sampling, Over-identified Parameter Problems and Beyond$b[electronic resource] /$fby Jing Qin
205   $a1st ed. 2017.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2017.
215   $a1 online resource (XVI, 624 p. 5 illus., 1 illus. in color.)
225 1 $aICSA Book Series in Statistics,$x2199-0980
311   $a981-10-4854-1 
320   $aIncludes bibliographical references and index.
327   $aChapter 1. Some Examples on Biased Sampling Problems -- Chapter 2. Some Results in Parametric Likelihood and Estimating Functions -- Chapter 3. Nonparametric Maximum Likelihood Estimation and Empirical Likelihood Method -- Chapter 4. General Results in Multiple Samples Biased Sampling Problems with Applications in Case and Control and Genetic Epidemiology -- Chapter 5. Outcome Dependent Sampling Problems -- Chapter 6. Missing Data Problem and Causal Inference -- Chapter 7. Applications of Exponential Tilting Models in Finite Mixture Models -- Chapter 8. Applications of Empirical Likelihood Methods in Survey Sampling -- Chapter 9. Some Other Topics.
330   $aThis book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. .
410  0$aICSA Book Series in Statistics,$x2199-0980
606   $aStatistics 
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aEconomic theory
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
606   $aApplications of Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M13003
606   $aEconomic Theory/Quantitative Economics/Mathematical Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/W29000
615  0$aStatistics .
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aEconomic theory.
615 14$aStatistics for Business, Management, Economics, Finance, Insurance.
615 24$aApplications of Mathematics.
615 24$aEconomic Theory/Quantitative Economics/Mathematical Methods.
676   $a519.52
700   $aQin$b Jing$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767163
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254276103321
996   $aBiased Sampling, Over-identified Parameter Problems and Beyond$91561697
997   $aUNINA