An application of Cheng's lemma for minimizing the asymptotic variance of best asymptotically normal estimators based on sample quantiles

DOI10.1016/0378-3758(90)90138-KzbMath0709.62028WikidataQ124817355 ScholiaQ124817355MaRDI QIDQ921768

Steven G. From

Publication date: 1990

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

monotone likelihood ratiosample quantilesBAN-estimatorslinear failure rate distributionoptimal spacings for non-location, non-scale parametersoptimal spacings of best asymptotically normal estimators

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10) Order statistics; empirical distribution functions (62G30)

Related Items (2)

Bounding maximum likelihood estimates based on incomplete ordered data ⋮ Posterior computations based on sample quantiles: one- and two-parameter exponential cases

Cites Work

• Unnamed Item
• A density-quantile function approach to optimal spacing selection
• Optimal spacing of quantiles for the estimation of the mixing parameters in a mixture of two exponential distributions
• Asymptotic Properties of a Family of Minimum Quantile Distance Estimators
• Optimal Grouping, Spacing, Stratification, and Piecewise Constant Approximation
• A Unified Approach to Choosing Optimum Quantiles for the ABLE's

This page was built for publication: An application of Cheng's lemma for minimizing the asymptotic variance of best asymptotically normal estimators based on sample quantiles