Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/78420
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Gao, F | en_US |
dc.creator | Xiong, J | en_US |
dc.creator | Zhao, X | en_US |
dc.date.accessioned | 2018-09-28T01:16:29Z | - |
dc.date.available | 2018-09-28T01:16:29Z | - |
dc.identifier.issn | 0090-5364 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/78420 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Mathematical Statistics | en_US |
dc.rights | © Institute of Mathematical Statistics, 2018 | en_US |
dc.rights | The following publication Gao, F., Xiong, J., & Zhao, X. (2018). Moderate deviations and nonparametric inference for monotone functions. The Annals of Statistics, 46(3), 1225-1254 is available at https://dx.doi.org/10.1214/17-AOS1583 | en_US |
dc.subject | Grenander estimator | en_US |
dc.subject | Interval censored data | en_US |
dc.subject | Large deviations | en_US |
dc.subject | Moderate deviations | en_US |
dc.subject | Nonparametric MLE | en_US |
dc.subject | Self-normalized limit | en_US |
dc.subject | Strong approximation | en_US |
dc.subject | Talagrand inequality | en_US |
dc.title | Moderate deviations and nonparametric inference for monotone functions | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1225 | en_US |
dc.identifier.epage | 1254 | en_US |
dc.identifier.volume | 46 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1214/17-AOS1583 | en_US |
dcterms.abstract | This paper considers self-normalized limits and moderate deviations of nonparametric maximum likelihood estimators for monotone functions. We obtain their self-normalized Cramer-type moderate deviations and limit distribution theorems for the nonparametric maximum likelihood estimator in the current status model and the Grenander-type estimator. As applications of the results, we present a new procedure to construct asymptotical confidence intervals and asymptotical rejection regions of hypothesis testing for monotone functions. The theoretical results can guarantee that the new test has the probability of type II error tending to 0 exponentially. Simulation studies also show that the new nonparametric test works well for the most commonly used parametric survival functions such as exponential and Weibull survival distributions. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Annals of statistics, June 2018, v. 46, no. 3, p. 1225-1254 | en_US |
dcterms.isPartOf | Annals of statistics | en_US |
dcterms.issued | 2018-06 | - |
dc.identifier.isi | WOS:000435502200011 | - |
dc.identifier.rosgroupid | 2017000682 | - |
dc.description.ros | 2017-2018 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201809 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | AMA-0378 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6842030 | - |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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17-AOS1583.pdf | 291.57 kB | Adobe PDF | View/Open |
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