Bias reduction of maximum likelihood estimators using kernel estimators

DOI10.1080/02331889008802268zbMath0714.62022OpenAlexW2026224381WikidataQ126241940 ScholiaQ126241940MaRDI QIDQ3200386

Hendrik Schäbe

Publication date: 1990

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889008802268

zbMATH Keywords

bias reductionkernel estimatorWeibull distributionmodified likelihoodcensored samplesuncensored samplesbias of maximum likelihood estimatesbias of order o(1/n)functional of the empirical distribution function

Mathematics Subject Classification ID

Point estimation (62F10) Bayesian inference (62F15) Foundations and philosophical topics in statistics (62A01)

Cites Work

• Unnamed Item
• Third-order optimum properties of estimator-sequences
• Expansions for the distribution and quantiles of a regular functional of the empirical distribution with applications to nonparametric confidence intervals
• The consistency of automatic kernel density estimates
• Rates of convergence for the distance between distribution function estimators
• A comparison of cross-validation techniques in density estimation
• Likelihood and observed geometries
• Differential geometry of curved exponential families. Curvatures and information loss
• A large sample study of the life table and product limit estimates under random censorship
• Relative efficiency and deficiency of kernel type estimators of smooth distribution functions
• Developments in Nonparametric Density Estimation
• A Simulation Study of Estimators for the 2-Parameter Weibull Distribution
• Algorithm AS 176: Kernel Density Estimation Using the Fast Fourier Transform

This page was built for publication: Bias reduction of maximum likelihood estimators using kernel estimators