EMPIRICAL LIKELIHOOD-BASED INFERENCES FOR PARTIALLY LINEAR MODELS WITH MISSING COVARIATES

DOI10.1111/j.1467-842X.2008.00521.xzbMath1337.62181OpenAlexW2159083513WikidataQ42552660 ScholiaQ42552660MaRDI QIDQ2810363

Hua Liang, Yong Song Qin

Publication date: 1 June 2016

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-842x.2008.00521.x

zbMATH Keywords

confidence regionsemiparametric estimationmissing at randomlocal linear regression

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items

Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables ⋮ Empirical likelihood semiparametric nonlinear regression analysis for longitudinal data with responses missing at random ⋮ A review on empirical likelihood methods for regression ⋮ ANOVA for longitudinal data with missing values ⋮ Testing for parametric component of partially linear models with missing covariates ⋮ Empirical likelihood for the response mean of generalized linear models with missing at random responses ⋮ Testing heteroscedasticity in partially linear models with missing covariates ⋮ Empirical likelihood-based inferences in varying coefficient models with missing data ⋮ Generalized Partially Linear Models for Incomplete Longitudinal Data In the Presence of Population‐Level Information

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