Maximum likelihood estimation of dependence parameter using ranked set sampling

DOI10.1016/j.spl.2004.04.003zbMath1075.62020OpenAlexW2046553550MaRDI QIDQ1771431

Reza Modarres, Gang Zheng

Publication date: 21 April 2005

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2004.04.003

zbMATH Keywords

CorrelationFisher informationParametric bootstrapBivariate distributionImperfect ranking

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Point estimation (62F10) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (8)

Maximum likelihood estimator of the parameter for a continuous one-parameter exponential family under the optimal ranked set sampling ⋮ Fisher information in record values and their concomitants about dependence and correlation parameters ⋮ Inference on skew-normal distribution based on Fisher information in order statistics ⋮ Unnamed Item ⋮ Inferences on stress–strength reliability based on ranked set sampling data in case of Lindley distribution ⋮ Estimation of a parameter of Morgenstern type bivariate exponential distribution by ranked set sampling ⋮ Gumbel's bivariate exponential distribution: estimation of the association parameter using ranked set sampling ⋮ Inferences for stress–strength reliability of Burr Type X distributions based on ranked set sampling

Cites Work

• Unnamed Item
• Unnamed Item
• On the theory of ranked-set sampling and its ramifications
• Fisher information in an order statistic and its concomitant
• Bivariate Exponential Distributions
• Ranked Set Sampling Theory with Order Statistics Background
• Inferences on the Correlation Coefficient in Bivariate Normal Populations From Ranked Set Samples
• Fisher Information for a Bivariate Extreme Value Distribution
• Concomitants and correlation estimates
• U-Statistics and imperfect ranking in ranked set sampling
• Regression Estimator in Ranked Set Sampling
• Theory & Methods: Estimation of bivariate characteristics using ranked set sampling

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