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- Title
Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.
- Authors
Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi
- Abstract
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.
- Subjects
INFERENTIAL statistics; NONPARAMETRIC estimation; MAXIMUM likelihood statistics; DISTRIBUTION (Probability theory); STATISTICAL bootstrapping; CONFIDENCE intervals
- Publication
Lifetime Data Analysis, 2015, Vol 21, Issue 3, p397
- ISSN
1380-7870
- Publication type
Article
- DOI
10.1007/s10985-014-9297-5