Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95424
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Hao, M | en_US |
dc.creator | Lin, Y | en_US |
dc.creator | Liu, KY | en_US |
dc.creator | Zhao, X | en_US |
dc.date.accessioned | 2022-09-19T02:00:50Z | - |
dc.date.available | 2022-09-19T02:00:50Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/95424 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Mathematical Statistics | en_US |
dc.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/). | en_US |
dc.rights | 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. | en_US |
dc.subject | Current status data | en_US |
dc.subject | Functional Bahadur rep-resentation | en_US |
dc.subject | Likelihood ratio test | en_US |
dc.subject | Nonparametric inference | en_US |
dc.subject | Penalized likeli-hood | en_US |
dc.title | Penalized nonparametric likelihood-based inference for current status data model | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 3099 | en_US |
dc.identifier.epage | 3134 | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1214/21-EJS1970 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Electronic journal of statistics, 2022, v. 16, no. 1, p. 3099-3134 | en_US |
dcterms.isPartOf | Electronic journal of statistics | en_US |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85130587704 | - |
dc.identifier.ros | 2021003992 | - |
dc.identifier.eissn | 1935-7524 | en_US |
dc.description.validate | 202209 bchy | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | CDCF_2021-2022, a2342a | en_US |
dc.identifier.SubFormID | 47544 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Program for Young Excellent Talents; The Chinese University of Hong Kong; The Hong Kong Polytechnic University. | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Liu_Penalized_nonparametric_likelihood-based.pdf | 480 kB | Adobe PDF | View/Open |
Page views
81
Last Week
0
0
Last month
Citations as of Oct 13, 2024
Downloads
41
Citations as of Oct 13, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.