Kernel-type density and failure rate estimation for associated sequences

DOI10.1007/BF00773461zbMath0833.62036OpenAlexW1996186564MaRDI QIDQ1901674

Isha Bagai, B. L. S. Prakasa Rao

Publication date: 8 November 1995

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00773461

zbMATH Keywords

kernel estimatorsstrong uniform consistencydensity estimatorpointwise strong consistencyempirical survival functionfailure-rate estimatorstrictly stationary sequence of associated random variables

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Reliability and life testing (62N05)

Related Items

A general method of density estimation for associated random variables, Density estimation for associated sampling: A point process influenced approach, Remarks on the strong law of large numbers for a triangular array of associated random variables, Asymptotic results for truncated-censored and associated data, Kernel density estimation under negative superadditive dependence and its application for real data, Density deconvolution with associated stationary data., Confidence intervals for probability density functions under associated samples, Exponential inequality for associated random variables, A triangular central limit theorem under a new weak dependence condition, Estimation of the survival function for stationary associated processes, An Asymmetric Kernel Estimator of Density Function for Stationary Associated Sequences, An Esseen-type inequality for probability density functions, with an application, Asymptotic normality for \(U\)-statistics of associated random variables, Exponential rates for kernel density estimation under association, On kernel density and mode estimates for associated and censored data, Empirical likelihood for quantiles under associated samples, Convergence rate of the kernel regression estimator for associated and truncated data

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