 Weighted empirical likelihood inferenceChangbao Wu Statistics & Probability Letters, 2004, vol. 66, issue 1, 67-79 Abstract: A weighted empirical likelihood approach is proposed to take account of the heteroscedastic structure of the data. The resulting weighted empirical likelihood ratio statistic is shown to have a limiting chisquare distribution. A limited simulation study shows that the associated confidence intervals for a population mean or a regression coefficient have more accurate coverage probabilities and more balanced two-sided tail errors when the sample size is small or moderate. The proposed weighted empirical likelihood method also provides more efficient point estimators for a population mean in the presence of side information. Large sample resemblances between the weighted and the unweighted empirical likelihood estimators are characterized through high-order asymptotics and small sample discrepancies of these estimators are investigated through simulation. The proposed weighted approach reduces to the usual unweighted empirical likelihood method under a homogeneous variance structure. Keywords: Confidence; interval; Finite; population; Heteroscedasticity; Linear; regression; model; Minimum; entropy; distance; Point; estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:66:y:2004:i:1:p:67-79 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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