On the Edgeworth expansion and bootstrap approximation for the cox regression model under random censorship

DOI10.2307/3315610zbMath0765.62023OpenAlexW2095763213MaRDI QIDQ4036390

Ming Gao Gu

Publication date: 16 May 1993

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315610

zbMATH Keywords

bootstrap approximationasymptotic representationmartingalesurvival dataone-term Edgeworth expansionCox semiparametric regression modelsecond-order correct estimates

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric statistical resampling methods (62G09)

Related Items (9)

EMPIRICAL LIKELIHOOD FOR COX REGRESSION MODEL UNDER RANDOM CENSORSHIP ⋮ Edgeworth expansion in censored linear regression model ⋮ Goodness-of-fit tests for the Cox model via bootstrap method ⋮ Confidence intervals for survival quantiles in the Cox regression model ⋮ Cumulants and Bartlett Identities in Cox Regression ⋮ Jackknifed random weighting for Cox proportional hazards model ⋮ A Bartlett type correction for Wald test in Cox regression model ⋮ Likelihood and higher‐order approximations to tail areas: A review and annotated bibliography ⋮ Principled sure independence screening for Cox models with ultra-high-dimensional covariates

Cites Work

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• Cox's regression model for counting processes: A large sample study
• On the Edgeworth expansion and the bootstrap approximation for a Studentized U-statistic
• Theoretical comparison of bootstrap confidence intervals
• A large sample study of Cox's regression model
• The empirical characteristic function and its applications
• The asymptotic joint distribution of regression and survival parameter estimates in the Cox regression model
• Understanding Cox's Regression Model: A Martingale Approach
• Splitting a Single State of a Stationary Process into Markovian States

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