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150323s1988 xx |||||o 00| ||eng c |
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|a (DE-627)JST00899644X
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|a (JST)2241619
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a 62G05
|2 MSC
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|a 60F05
|2 MSC
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|a 62G30
|2 MSC
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|a 60G44
|2 MSC
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|a Gill, Richard D.
|e verfasserin
|4 aut
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|a Large Sample Theory of Empirical Distributions in Biased Sampling Models
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|c 1988
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|a Text
|b txt
|2 rdacontent
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|a Computermedien
|b c
|2 rdamedia
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|a Online-Ressource
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|a Vardi (1985a) introduced an s-sample model for biased sampling, gave conditions which guarantee the existence and uniqueness of the nonparametric maximum likelihood estimator Gnof the common underlying distribution G and discussed numerical methods for calculating the estimator. Here we examine the large sample behavior of the NPMLE Gn, including results on uniform consistency of Gn, convergence of $\sqrt n (\mathbb{G}_n - G)$ to a Gaussian process and asymptotic efficiency of Gnas an estimator of G. The proofs are based upon recent results for empirical processes indexed by sets and functions and convexity arguments. We also give a careful proof of identifiability of the underlying distribution G under connectedness of a certain graph G. Examples and applications include length-biased sampling, stratified sampling, "enriched" stratified sampling, "choice-based" sampling in econometrics and "case-control" studies in biostatistics. A final section discusses design issues and further problems.
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|a Copyright 1988 Institute of Mathematical Statistics
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|a Asymptotic theory
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|a case-control studies
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|a choice based sampling
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|a empirical processes
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|a enriched stratified sampling
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|a graphs
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|a lenght-biased sampling
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|a Neyman allocation
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|a nonparametric maximum likelihood
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|a selection bias models
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|a stratified sampling
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|a Vardi's estimator
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|a Philosophy
|x Applied philosophy
|x Philosophy of science
|x Scientific method
|x Research variables
|x Research biases
|x Sampling bias
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
|x Estimators
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|a Mathematics
|x Applied mathematics
|x Analytics
|x Analytical estimating
|x Maximum likelihood estimation
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4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Measures of variability
|x Multivariate statistical analysis
|x Covariance
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4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Statistical theories
|x Estimation theory
|x Estimation bias
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650 |
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4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
|x Estimators
|x Maximum likelihood estimators
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|
4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Descriptive statistics
|x Statistical distributions
|x Normal distribution curve
|x Sampling distributions
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|
4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Statistical theories
|x Asymptotic theory
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650 |
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4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Statistical results
|x Statistical properties
|x Identifiability
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4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
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|a research-article
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1 |
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|a Vardi, Yehuda
|e verfasserin
|4 aut
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|a Wellner, Jon A.
|e verfasserin
|4 aut
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8 |
|i Enthalten in
|t The Annals of Statistics
|d Institute of Mathematical Statistics
|g 16(1988), 3, Seite 1069-1112
|w (DE-627)270129162
|w (DE-600)1476670-X
|x 00905364
|7 nnns
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|g volume:16
|g year:1988
|g number:3
|g pages:1069-1112
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|u https://www.jstor.org/stable/2241619
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|d 16
|j 1988
|e 3
|h 1069-1112
|