Modeling of discrete questionnaire data with dimension reduction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00557126" target="_blank" >RIV/67985556:_____/22:00557126 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/22:00357903
Result on the web
<a href="http://nnw.cz/doi/2022/NNW.2022.32.002.pdf" target="_blank" >http://nnw.cz/doi/2022/NNW.2022.32.002.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2022.32.002" target="_blank" >10.14311/NNW.2022.32.002</a>
Alternative languages
Result language
angličtina
Original language name
Modeling of discrete questionnaire data with dimension reduction
Original language description
The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
<a href="/en/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
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Volume of the periodical
32
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
27
Pages from-to
15-41
UT code for WoS article
000795530700002
EID of the result in the Scopus database
2-s2.0-85130701278