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Estimating a probab...
Estimating a probability mass function with unknown labels
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- Anevski, Dragi (författare)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Gill, Richard D. (författare)
- Leiden University
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- Zohren, Stefan (författare)
- University of Oxford
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(creator_code:org_t)
- 2017
- 2017
- Engelska 28 s.
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Ingår i: Annals of Statistics. - 0090-5364. ; 45:6, s. 2708-2735
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In the context of a species sampling problem, we discuss a nonparametric maximum likelihood estimator for the underlying probability mass function. The estimator is known in the computer science literature as the high profile estimator. We prove strong consistency and derive the rates of convergence, for an extended model version of the estimator. We also study a sieved estimator for which similar consistency results are derived. Numerical computation of the sieved estimator is of great interest for practical problems, such as forensic DNA analysis, and we present a computational algorithm based on the stochastic approximation of the expectation maximisation algorithm. As an interesting byproduct of the numerical analyses, we introduce an algorithm for bounded isotonic regression for which we also prove convergence.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- High profile
- Monotone rearrangement
- Nonparametric
- NPMLE
- Ordered
- Probability mass function
- Rates
- SA-EM
- Sieve
- Strong consistency
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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