Modeling of discrete questionnaire data with dimension reduction

0557126 - ÚTIA 2023 RIV CZ eng J - Journal Article
Jozová, Šárka - Uglickich, Evženie - Nagy, Ivan - Likhonina, Raissa
Modeling of discrete questionnaire data with dimension reduction.
Neural Network World. Roč. 32, č. 1 (2022), s. 15-41. ISSN 1210-0552
R&D Projects: GA MŠMT(CZ) 8A19009
Institutional support: RVO:67985556
Keywords : questionnaire data analysis * dimension reduction * binomial mixture * recursive Bayesian mixture estimation * accident severity
OECD category: Statistics and probability
Impact factor: 0.8, year: 2022
Method of publishing: Open access
http://library.utia.cas.cz/separaty/2022/ZS/uglickich-0557126.pdf http://nnw.cz/doi/2022/NNW.2022.32.002.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0331259