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http://hdl.handle.net/10397/95424
Title: | Penalized nonparametric likelihood-based inference for current status data model | Authors: | Hao, M Lin, Y Liu, KY Zhao, X |
Issue Date: | 2022 | Source: | Electronic journal of statistics, 2022, v. 16, no. 1, p. 3099-3134 | Abstract: | Deriving the limiting distribution of a nonparametric estimate is rather challenging but of fundamental importance to statistical inference. For the current status data, we study a penalized nonparametric likelihood-based estimator for an unknown cumulative hazard function, and establish the pointwise asymptotic normality of the resulting nonparametric esti-mate. We also propose the penalized likelihood ratio tests for local and global hypotheses, derive their limiting distributions, and study the opti-mality of the global test. Simulation studies show that the proposed method works well compared to the classical likelihood ratio test. | Keywords: | Current status data Functional Bahadur rep-resentation Likelihood ratio test Nonparametric inference Penalized likeli-hood |
Publisher: | Institute of Mathematical Statistics | Journal: | Electronic journal of statistics | EISSN: | 1935-7524 | DOI: | 10.1214/21-EJS1970 | Rights: | This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). The following publication Meiling Hao. Yuanyuan Lin. Kin-yat Liu. Xingqiu Zhao. "Penalized nonparametric likelihood-based inference for current status data model." Electron. J. Statist. 16 (1) 3099 - 3134, 2022 is available at https://doi.org/10.1214/21-EJS1970. |
Appears in Collections: | Journal/Magazine Article |
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