Cai Jianwen
Department of Biostatistics
University of North Carolina at Chapel Hill
Marginal Hazard Models with Varying-coefficients for Multivariate
Failure Time Data
Statistical inference for the marginal hazard models with
varying-coefficients for multivariate failure time data is studied. A local
pseudo-partial likelihood procedure is proposed for estimating the unknown
coefficients and the intercept function. A weighted average estimator is
also proposed in an attempt to improve the efficiency of the estimator. The
consistency and the asymptotic normality of the proposed estimators are
established and the standard error formulas for the estimated coefficients
are derived and empirically tested. To reduce the computational burden of
the maximum local pseudo-partial likelihood estimator, a simple and useful
one-step estimator is proposed. Statistical properties of one-step estimator
is established and simulation studies are conducted to compare the
performance of the one-step estimator to the maximum local pseudo-partial
likelihood estimator. The results show that the one-step estimator can save
computational cost without deteriorating its performance both asymptotically
and empirically. A data set from the Busselton Population Health Surveys is
analyzed to illustrate our proposed methodology.
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