Semiparametric Likelihood-Based Inference for Biased and Truncated Data When the Total Sample Size is Known

DOI10.1111/1467-9868.00122zbMath0909.62025OpenAlexW2073202163MaRDI QIDQ4214234

Gang Li, Jing Qin

Publication date: 18 October 1998

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9868.00122

zbMATH Keywords

reliabilitytruncationnatural mortalityauxiliary informationsample surveyssampling biassemiparametric likelihood ratio

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Related Items (6)

An overview of nonparametric contributions to the problem of functional estimation from biased data ⋮ Augmented likelihood for incorporating auxiliary information into left-truncated data ⋮ Nonparametric estimation with left-truncated and right-censored data when the sample size before truncation is known ⋮ Semiparametric likelihood-based inference for censored data with auxiliary information from external massive data sources ⋮ Depth-based weighted empirical likelihood and general estimating equations ⋮ Hypothesis testing for the population mean and variance based on r-size biased samples

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