Asymptotic results for stopping times based on u-statistics

DOI10.1080/07474948508836073zbMath0582.62074OpenAlexW2007578126MaRDI QIDQ3704769

Nitis Mukhopadhyay, G. Vik

Publication date: 1985

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11244/18941

zbMATH Keywords

asymptotic normalityasymptotic behaviourrates of convergencestopping timestwo-stage proceduresequential fixed-width confidence intervalsmean of U-statistics

Mathematics Subject Classification ID

Sequential estimation (62L12)

Related Items (10)

U-Statistics in Sequential Tests and Change Detection ⋮ Sequential simultaneous estimation of the mean and variance: The rate of convergence ⋮ On sequential fixed-width confidence intervals for the mean and second-order expansions of the associated coverage probabilities ⋮ The bootstrap applied to sequential analysis ⋮ Convergence rates for two-stage confidence intervals based on U- statistics ⋮ Sequential estimation of the difference of means of two negative exponential populations ⋮ EDA on the asymptotic normality of the standardized sequential stopping times, Part-I: Parametric models ⋮ An overview of sequential nonparametric density estimation ⋮ Two-stage estimation for a normal mean having a known lower bound of variance with final sample size defined via Gini’s mean difference and mean absolute deviation ⋮ EDA on the asymptotic normality of the standardized sequential stopping times, Part-II: Distribution-free models

Cites Work

• On the convergence rate of fixed-width sequential confidence intervals
• Sequential point estimation of the mean when the distribution is unspecified
• Convergence rates of sequential confidence intervals and tests for the mean of a u-statistic
• The exact approximation order in the central-limit-theorem for random summation
• On the Asymptotic Theory of Fixed-Width Sequential Confidence Intervals for the Mean
• Linear Statistical Inference and its Applications
• A Class of Statistics with Asymptotically Normal Distribution
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