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Sequential Good-Turing and the Missing Species Problem

Andersson, Oskar LU (2022) In Master's Theses in Mathematical Sciences MASM02 20221
Mathematical Statistics
Abstract (Swedish)
This essay introduces the sequential Good-Turing estimator and reviews the
Good-Turing, Good-Toulmin and smoothed Good-Toulmin estimators. Some
theoretical properties and drawbacks of the estimators are described. MonteCarlo simulation is then used to compare the performance of the sequential
Good-Turing estimator to the performance of the Good-Toulmin estimator
along with the smoothed Good-Toulmin estimator, on both real and simulated data.
In certain scenarios the Monte-Carlo method outperforms the smoothed
Good-Toulmin estimator.
Please use this url to cite or link to this publication:
author
Andersson, Oskar LU
supervisor
organization
course
MASM02 20221
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Good Turing, The missing Species problem, Good Toulmin, unseen species
publication/series
Master's Theses in Mathematical Sciences
report number
LUNFMS-3113-2022
ISSN
1404-6342
other publication id
2022:E54
language
English
id
9093658
date added to LUP
2022-08-15 17:49:04
date last changed
2022-08-15 18:10:32
@misc{9093658,
  abstract     = {{This essay introduces the sequential Good-Turing estimator and reviews the
Good-Turing, Good-Toulmin and smoothed Good-Toulmin estimators. Some
theoretical properties and drawbacks of the estimators are described. MonteCarlo simulation is then used to compare the performance of the sequential
Good-Turing estimator to the performance of the Good-Toulmin estimator
along with the smoothed Good-Toulmin estimator, on both real and simulated data.
In certain scenarios the Monte-Carlo method outperforms the smoothed
Good-Toulmin estimator.}},
  author       = {{Andersson, Oskar}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Sequential Good-Turing and the Missing Species Problem}},
  year         = {{2022}},
}