Springer Netherlands;New York: Springer Science and Business Media LLC
摘要:
摘要： 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. 其他題名： Lifetime Data Anal 出版者： New York: Springer Science and Business Media LLC 出版日期： 2015-07-01 出處： Lifetime Data Analysis, 2015-07, Vol.21 (3), p.397-418 資源來源： Healthcare Administration Database (Proquest) 版權： Springer Science+Business Media New York 2014 版權： Springer Science+Business Media New York 2015 識別號： ISSN: 1380-7870 識別號： ISSN: 1572-9249 識別號： EISSN: 1572-9249 識別號： DOI: 10.1007/s10985-014-9297-5 識別號： PMID: 25001399
