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dc.contributor.author
Balabdaoui, Fadoua
dc.contributor.author
Besdziek, Harald
dc.date.accessioned
2023-11-28T08:21:09Z
dc.date.available
2023-11-28T06:11:39Z
dc.date.available
2023-11-28T08:21:09Z
dc.date.issued
2024-05
dc.identifier.issn
0378-3758
dc.identifier.issn
1873-1171
dc.identifier.other
10.1016/j.jspi.2023.106113
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/644110
dc.description.abstract
The two-component mixture model with known background density, unknown signal density, and unknown mixing proportion has been studied in many contexts. One such context is multiple testing, where the background and signal densities describe the distribution of the p-values under the null and alternative hypotheses, respectively. In this paper, we consider the log-concave MLE of the signal density using the estimator of Patra & Sen (2016) for the mixing probability. We show that it is consistent and converges at the global rate n⁻²/⁵. An EM-algorithm in combination with an active set algorithm implemented in the R-package logcondens was used to compute the log-concave MLE. When one is interested in estimation at a fixed point, a conjecture is made about the limit distribution of our estimator. The performance of our method is assessed through a simulation study.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Empirical processes
en_US
dc.subject
Log-concave
en_US
dc.subject
Maximum likelihood
en_US
dc.subject
Mixture
en_US
dc.subject
Rate of convergence
en_US
dc.title
Maximum likelihood estimation of the log-concave component in a semi-parametric mixture with a standard normal density
en_US
dc.type
Journal Article
dc.date.published
2023-10-07
ethz.journal.title
Journal of Statistical Planning and Inference
ethz.journal.volume
230
en_US
ethz.journal.abbreviated
J. Stat. Plan. Inference
ethz.pages.start
106113
en_US
ethz.size
29 p.
en_US
ethz.grant
Mixture models for discrete data: general asymptotic theory for the nonparametric maximum likelihood estimator and statistical applications
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.status
published
en_US
ethz.grant.agreementno
191999
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.date.deposited
2023-11-28T06:11:43Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-11-28T08:21:10Z
ethz.rosetta.lastUpdated
2024-02-03T07:14:52Z
ethz.rosetta.versionExported
true
ethz.COinS
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