Sample Size for Maximum-Likelihood Estimates of Gaussian Model Depending on Dimensionality of Pattern Space

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43955842" target="_blank" >RIV/49777513:23520/19:43955842 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0031320319300445" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0031320319300445</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patcog.2019.01.046" target="_blank" >10.1016/j.patcog.2019.01.046</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sample Size for Maximum-Likelihood Estimates of Gaussian Model Depending on Dimensionality of Pattern Space

Popis výsledku v původním jazyce

The significant properties of the maximum likelihood (ML) estimate are consistency, normality, and efficiency. While it has been proven that these properties are valid when the sample size approaches infinity, the behavior of an ML estimator when working with small sample sizes is largely unknown. However, in real tasks, we usually do not have sufficient data to completely fulfill the conditions of an optimal ML estimate. The question arises as to what amount of data is required to be able to estimate a Gaussian model that provides sufficiently accurate likelihood estimates. This issue is addressed with respect to the number of dimensions of the pattern space.

Název v anglickém jazyce

Sample Size for Maximum-Likelihood Estimates of Gaussian Model Depending on Dimensionality of Pattern Space

Popis výsledku anglicky

The significant properties of the maximum likelihood (ML) estimate are consistency, normality, and efficiency. While it has been proven that these properties are valid when the sample size approaches infinity, the behavior of an ML estimator when working with small sample sizes is largely unknown. However, in real tasks, we usually do not have sufficient data to completely fulfill the conditions of an optimal ML estimate. The question arises as to what amount of data is required to be able to estimate a Gaussian model that provides sufficiently accurate likelihood estimates. This issue is addressed with respect to the number of dimensions of the pattern space.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PATTERN RECOGNITION

ISSN

0031-3203

e-ISSN

—

Svazek periodika

91

Číslo periodika v rámci svazku

July 2019

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

25-33

Kód UT WoS článku

000466250400003

EID výsledku v databázi Scopus

2-s2.0-85061702496