Model Choice for Regression Models with a Categorical Response

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00558999" target="_blank" >RIV/67985807:_____/22:00558999 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:10454230

Výsledek na webu

<a href="https://dx.doi.org/10.2478/jamsi-2022-0005" target="_blank" >https://dx.doi.org/10.2478/jamsi-2022-0005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/jamsi-2022-0005" target="_blank" >10.2478/jamsi-2022-0005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model Choice for Regression Models with a Categorical Response

Popis výsledku v původním jazyce

The multinomial logit model and the cumulative logit model represent two important tools for regression modeling with a categorical response with numerous applications in various fields. First, this paper presents a systematic review of these two models including available tools for model choice (model selection). Then, numerical experiments are presented for two real datasets with an ordinal categorical response. These experiments reveal that a backward model choice procedure by means of hypothesis testing is more effective compared to a procedure based on Akaike information criterion. While the tendency of the backward selection to be superior to Akaike information criterion has recently been justified in linear regression, such a result seems not to have been presented for models with a categorical response. In addition, we report a mistake in VGAM package of R software, which has however no influence on the process of model choice.

Název v anglickém jazyce

Model Choice for Regression Models with a Categorical Response

Popis výsledku anglicky

The multinomial logit model and the cumulative logit model represent two important tools for regression modeling with a categorical response with numerous applications in various fields. First, this paper presents a systematic review of these two models including available tools for model choice (model selection). Then, numerical experiments are presented for two real datasets with an ordinal categorical response. These experiments reveal that a backward model choice procedure by means of hypothesis testing is more effective compared to a procedure based on Akaike information criterion. While the tendency of the backward selection to be superior to Akaike information criterion has recently been justified in linear regression, such a result seems not to have been presented for models with a categorical response. In addition, we report a mistake in VGAM package of R software, which has however no influence on the process of model choice.

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/GA21-19311S" target="_blank" >GA21-19311S: Informační tok a ekvilibrium ve finančních trzích</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Journal of applied mathematics, statistics and informatics

ISSN

1336-9180

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

13

Strana od-do

59-71

Kód UT WoS článku

000820112700005

EID výsledku v databázi Scopus

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