Moderate and large deviations for the smoothed estimate of sample quantiles

DOI10.1155/2015/714201zbMath1426.60033OpenAlexW1592323044WikidataQ59114238 ScholiaQ59114238MaRDI QIDQ1657911

Xi Liu, Chun Yao, Xiaoxia He

Publication date: 14 August 2018

Published in: Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/714201

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Order statistics; empirical distribution functions (62G30) Large deviations (60F10)

Cites Work

• Unnamed Item
• Unnamed Item
• Relative deficiency of kernel type estimators of quantiles
• Asymptotic properties of sample quantiles of discrete distributions
• Moderate deviations and large deviations for a test of symmetry based on kernel density estimator
• A Berry-Esseen theorem for sample quantiles under weak dependence
• Estimation of quantiles in certain nonparametric models
• Smooth estimate of quantiles under association
• Moderate deviations and large deviations for kernel density estimators
• Moderate and large deviation principles for the hazard rate function kernel estimator under censoring
• On the Bahadur representation of sample quantiles and order statistics for NA sequences
• On the Bahadur representation of sample quantiles for dependent sequences
• Asymptotic Properties of the Deviation Between Order Statistics and p-Quantile
• A Smooth Nonparametric Estimator of a Quantile Function
• A Kernel-Type Estimator of a Quantile Function From Right-Censored Data
• Large Deviations Limit Theorems for the Kernel Density Estimator
• Nonparametric Statistical Data Modeling
• Limit behaviors of the deviation between the sample quantiles and the quantile
• Some New Estimates for Distribution Functions

This page was built for publication: Moderate and large deviations for the smoothed estimate of sample quantiles