Lifetime Data Anal - Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.

Tópicos

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Resumo

Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.

Resumo Limpo

doubli truncat data consist sampl whose observ valu fall right left truncat limit sampl distribut function interest estim use nonparametr maximum likelihood estim npmle obtain selfconsist algorithm owe complic asymptot distribut npmle bootstrap method suggest statist infer paper propos closedform estim asymptot covari function npmle comput attract altern bootstrap furthermor develop various statist infer procedur confid interv goodnessoffit test confid band demonstr use propos covari estim simul perform compar propos method bootstrap jackknif method method illustr use childhood cancer dataset

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