Model Choice for Regression Models with a Categorical Response

Result code in 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>

Alternative codes found

RIV/00216208:11320/22:10454230

Result on the web

<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>

Result language

angličtina

Original language name

Model Choice for Regression Models with a Categorical Response

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA21-19311S" target="_blank" >GA21-19311S: Information Flow and Equilibrium in Financial Markets</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Journal of applied mathematics, statistics and informatics

ISSN

1336-9180

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

1

Country of publishing house

SK - SLOVAKIA

Number of pages

13

Pages from-to

59-71

UT code for WoS article

000820112700005

EID of the result in the Scopus database

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