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  • Search: subject:"Nonignorable missing data"
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Year of publication
Subject
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nonignorable missing data 3 Imputation 2 Nonignorable missing data mechanism 2 Bayes factor 1 Empirical likelihood 1 Exponential tilting 1 Gibbs sampler 1 Item nonresponse 1 Measurement error 1 Metropolis–Hastings algorithm 1 Mixed-logit models 1 Multinomial logit models 1 NELS:88 1 Nonignorable missing data 1 Probabilistic sampling 1 Propensity score weighting 1 Response mean functional 1 Scanner panel data 1 Unit nonresponse 1 likelihood 1 longitudinal 1 longitudinal binary data 1 marginalized model 1 misspecification 1 model comparison 1 not missing at random 1 path sampling 1 simulation 1
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Online availability
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Undetermined 6
Type of publication
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Article 5 Book / Working Paper 1
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Undetermined 6
Author
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Bradlow, Eric 1 Durrant, Gabriele 1 Feldman, Betsy J. 1 Heagerty, Patrick 1 Kurland, Brenda 1 Lee, Sik-Yum 1 Rabe-Hesketh, Sophia 1 Tang, Nian-Sheng 1 Zanutto, Elaine 1 Zhao, Hui 1 Zhao, Pu-Ying 1
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Institution
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Berkeley Electronic Press 1
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AStA Advances in Statistical Analysis 1 Computational Statistics & Data Analysis 1 Journal of Educational and Behavioral Statistics 1 Psychometrika 1 Quantitative Marketing and Economics 1 UW Biostatistics Working Paper Series 1
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RePEc 6
Showing 1 - 6 of 6
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Empirical likelihood inference for mean functionals with nonignorably missing response data
Zhao, Hui; Zhao, Pu-Ying; Tang, Nian-Sheng - In: Computational Statistics & Data Analysis 66 (2013) C, pp. 101-116
An empirical likelihood (EL) approach to inference on mean functionals with nonignorably missing response data is developed. The nonignorably missing mechanism is specified by an exponential tilting model. Several maximum EL estimators (MELEs) for the response mean functional are proposed under...
Persistent link: https://www.econbiz.de/10010871331
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Modeling Achievement Trajectories When Attrition Is Informative
Feldman, Betsy J.; Rabe-Hesketh, Sophia - In: Journal of Educational and Behavioral Statistics 37 (2012) 6, pp. 703-736
In longitudinal education studies, assuming that dropout and missing data occur completely at random is often unrealistic. When the probability of dropout depends on covariates and observed responses (called missing at random [MAR]), or on values of responses that are missing (called informative...
Persistent link: https://www.econbiz.de/10011138710
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Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data
Lee, Sik-Yum - In: Psychometrika 71 (2006) 3, pp. 541-564
Persistent link: https://www.econbiz.de/10005603225
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Data pruning in consumer choice models
Zanutto, Elaine; Bradlow, Eric - In: Quantitative Marketing and Economics 4 (2006) 3, pp. 267-287
from current academic Marketing literature) can lead to a nonignorable missing data mechanism. Secondly, we summarize an …
Persistent link: https://www.econbiz.de/10005701807
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Missing data methods in official statistics in the United Kingdom: Some recent developments
Durrant, Gabriele - In: AStA Advances in Statistical Analysis 90 (2006) 4, pp. 577-593
Persistent link: https://www.econbiz.de/10005178809
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Marginalized Transition Models for Longitudinal Binary Data With Ignorable and Nonignorable Dropout
Kurland, Brenda; Heagerty, Patrick - Berkeley Electronic Press - 2004
burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and nonignorable missing data, and …
Persistent link: https://www.econbiz.de/10005579373
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