Least informative distributions in maximum q-log-likelihood estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00322787" target="_blank" >RIV/68407700:21340/18:00322787 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0378437118307283" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378437118307283</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.physa.2018.06.004" target="_blank" >10.1016/j.physa.2018.06.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Least informative distributions in maximum q-log-likelihood estimation

Popis výsledku v původním jazyce

We use the maximum q-log-likelihood estimation for Least informative distributions (LIDs) in order to estimate the parameters in probability density functions (PDFs) efficiently and robustly when data include outlier(s). LIDs are derived by using convex combinations of two PDFs. A convex combination of two PDFs is composed of an underlying distribution and a contamination. The optimal criterion is obtained by minimizing the change of maximum q-log-likelihood function when the data contain small amount of contamination. In this paper, we make a comparison between ordinary maximum likelihood estimation, maximum q-log-likelihood estimation (MqLE) and LIDs based on MqLE for parameter estimation from data with outliers. We derive a new Fisher information matrix based on the score function for LID from M-function and use it for choice of optimal estimator in the class of MqLE. The model selection is done by the robust information criteria. We test the methods on the real data with outliers and estimate shape and scale parameters of probability distributions. As a result, we show that the LIDs based on MqLE provide the most robust and efficient estimation of the model parameters.

Název v anglickém jazyce

Least informative distributions in maximum q-log-likelihood estimation

Popis výsledku anglicky

We use the maximum q-log-likelihood estimation for Least informative distributions (LIDs) in order to estimate the parameters in probability density functions (PDFs) efficiently and robustly when data include outlier(s). LIDs are derived by using convex combinations of two PDFs. A convex combination of two PDFs is composed of an underlying distribution and a contamination. The optimal criterion is obtained by minimizing the change of maximum q-log-likelihood function when the data contain small amount of contamination. In this paper, we make a comparison between ordinary maximum likelihood estimation, maximum q-log-likelihood estimation (MqLE) and LIDs based on MqLE for parameter estimation from data with outliers. We derive a new Fisher information matrix based on the score function for LID from M-function and use it for choice of optimal estimator in the class of MqLE. The model selection is done by the robust information criteria. We test the methods on the real data with outliers and estimate shape and scale parameters of probability distributions. As a result, we show that the LIDs based on MqLE provide the most robust and efficient estimation of the model parameters.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GF17-33812L" target="_blank" >GF17-33812L: Informačně-teoretický přístup ke komplexním dynamickým systémům</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Physica A: Statistical Mechanics and Its Applications

ISSN

0378-4371

e-ISSN

1873-2119

Svazek periodika

509

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

140-150

Kód UT WoS článku

000441492100012

EID výsledku v databázi Scopus

2-s2.0-85048778152