Confidence intervals for marginal parameters under fractional linear regression imputation for missing data
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Publication:928858
DOI10.1016/J.JMVA.2007.08.005zbMath1141.62018OpenAlexW2165564908MaRDI QIDQ928858
Qunshu Ren, J. N. K. Rao, Yong Song Qin
Publication date: 11 June 2008
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2007.08.005
Parametric tolerance and confidence regions (62F25) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Central limit and other weak theorems (60F05) Nonparametric tolerance and confidence regions (62G15)
Related Items (4)
Confidence intervals for nonparametric regression functions with missing data: multiple design case ⋮ Empirical likelihood inference for semi-parametric estimating equations ⋮ Hypothesis test on response mean with inequality constraints under data missing when covariables are present ⋮ On empirical likelihood for linear models with missing responses
Cites Work
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- Empirical likelihood ratio confidence regions
- On the asymptotic normality of statistics with estimated parameters
- Quantile estimation with a complex survey design
- Empirical likelihood-based inference under imputation for missing response data
- Pseudo-empirical likelihood ratio confidence intervals for complex surveys
- Fractional hot deck imputation
- On the Strong Law of Large Numbers and Related Results for Quasi-Stationary Sequences
- Empirical Likelihood-based Inference in Linear Models with Missing Data
- Empirical likelihood for linear regression models under imputation for missing responses
- A new estimation theory for sample surveys
- A New Proof of the Bahadur Representation of Quantiles and an Application
- Confidence Intervals for Medians and Other Position Measures
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