Theoretical and numerical aspects of the pattern maximum likelihood estimator, with a view towards symmetric functionals estimation
(2022) In Bachelor's Theses in Mathematicas Sciences MASK11 20221Mathematical Statistics
- Abstract
- Suppose that an infinite population is partitioned into different species. Given a random sample from the population of species, we are interested in estimating the species richness, which is the number of different species inhabiting a given area.
The species richness is a symmetric functional of the probability mass function. A suitable model for estimating the probability mass function is via the pattern maximum likelihood.
When the pattern maximum likelihood cannot be found analytically,
a sieved version of the pattern maximum likelihood can be used to find a numerical solution to the likelihood problem. The sieved pattern maximum likelihood estimator is consistent and can be calculated numerically using the SAEM algorithm.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9091178
- author
- Benthem Ciano, Paulina LU
- supervisor
- organization
- course
- MASK11 20221
- year
- 2022
- type
- M2 - Bachelor Degree
- subject
- publication/series
- Bachelor's Theses in Mathematicas Sciences
- report number
- LUNFMS-4065-2022
- ISSN
- 1654-6229
- other publication id
- 2022:K12
- language
- English
- id
- 9091178
- date added to LUP
- 2022-06-27 10:24:16
- date last changed
- 2022-06-30 14:14:51
@misc{9091178, abstract = {{Suppose that an infinite population is partitioned into different species. Given a random sample from the population of species, we are interested in estimating the species richness, which is the number of different species inhabiting a given area. The species richness is a symmetric functional of the probability mass function. A suitable model for estimating the probability mass function is via the pattern maximum likelihood. When the pattern maximum likelihood cannot be found analytically, a sieved version of the pattern maximum likelihood can be used to find a numerical solution to the likelihood problem. The sieved pattern maximum likelihood estimator is consistent and can be calculated numerically using the SAEM algorithm.}}, author = {{Benthem Ciano, Paulina}}, issn = {{1654-6229}}, language = {{eng}}, note = {{Student Paper}}, series = {{Bachelor's Theses in Mathematicas Sciences}}, title = {{Theoretical and numerical aspects of the pattern maximum likelihood estimator, with a view towards symmetric functionals estimation}}, year = {{2022}}, }