Inference for longitudinal data with nonignorable nonmonotone missing responses

Sanjoy K. Sinha; Amit Kaushal; Wenzhong Xiao

doi:10.1016/j.csda.2013.10.027

ScienceGate Book Chapters

JOURNAL ARTICLE

Inference for longitudinal data with nonignorable nonmonotone missing responses

Sanjoy K. Sinha Amit Kaushal Wenzhong Xiao

Year: 2013 Journal: Computational Statistics & Data Analysis Vol: 72 Pages: 77-91 Publisher: Elsevier BV

DOI: 10.1016/j.csda.2013.10.027

Get Full-Text PDF Get Analytical Report

Keywords:

Estimator Missing data Inference Monte Carlo method Computer science Computation Generalized estimating equation Longitudinal data Statistical inference Sampling (signal processing) Sample (material) Estimating equations Statistics Mathematics Algorithm Artificial intelligence Data mining Machine learning

Metrics

Cited By

0.59

FWCI (Field Weighted Citation Impact)

Refs

0.72

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods in Clinical Trials

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Inference for longitudinal data with nonignorable nonmonotone missing responses

Metrics

Citation History

Topics

Related Documents

Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference

Robust analysis of longitudinal data with nonignorable missing responses

LIKELIHOOD-BASED INFERENCE WITH NONIGNORABLE MISSING RESPONSES AND COVARIATES IN MODELS FOR DISCRETE LONGITUDINAL DATA

Likelihood-based inference with nonignorable missing responses and covariates in models for discrete longitudinal data

Analysis of longitudinal data under nonignorable nonmonotone nonresponse