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http://dx.doi.org/10.5351/KJAS.2020.36.6.753

Analysis of AI interview data using unified non-crossing multiple quantile regression tree model  

Kim, Jaeoh (Center for Army Analysis and Simulation, ROK Army HQs)
Bang, Sungwan (Department of Mathematics, Korea Military Academy)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.6, 2020 , pp. 753-762 More about this Journal
Abstract
With an increasing interest in integrating artificial intelligence (AI) into interview processes, the Republic of Korea (ROK) army is trying to lead and analyze AI-powered interview platform. This study is to analyze the AI interview data using a unified non-crossing multiple quantile tree (UNQRT) model. Compared to the UNQRT, the existing models, such as quantile regression and quantile regression tree model (QRT), are inadequate for the analysis of AI interview data. Specially, the linearity assumption of the quantile regression is overly strong for the aforementioned application. While the QRT model seems to be applicable by relaxing the linearity assumption, it suffers from crossing problems among estimated quantile functions and leads to an uninterpretable model. The UNQRT circumvents the crossing problem of quantile functions by simultaneously estimating multiple quantile functions with a non-crossing constraint and is robust from extreme quantiles. Furthermore, the single tree construction from the UNQRT leads to an interpretable model compared to the QRT model. In this study, by using the UNQRT, we explored the relationship between the results of the Army AI interview system and the existing personnel data to derive meaningful results.
Keywords
quantile regression; quantile regression tree; unified non-crossing multiple quantile regression tree; AI interview;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Bondell, H. D., Reich, B. J., and Wang, H. (2010). Noncrossing quantile regression curve estimation, Biometrika, 97, 825-838.   DOI
2 Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees, Wadsworth, Belmont.
3 Chang, Y. (2016). Variable selection with quantile regression tree, The Korean Journal of Applied Statistics, 29, 1095-1106.   DOI
4 Chaudhuri, P. and Loh, W. Y. (2002). Nonparametric estimation of conditional quantiles using quantile regression trees, Bernoulli, 8, 561-576.
5 Farcomeni, A. (2012). Quantile regression for longitudinal data based on latent Markov subject-specific parameters, Statistics and Computing, 22, 141-152.   DOI
6 Kim, J., Cho H., and Bang, S. (2019). Unified noncrossing multiple quantile regressions tree, Journal of Computational and Graphical Statistics, 28, 454-465.   DOI
7 Koenker, R. and Bassett Jr, G. (1978). Regression quantiles, Econometrica: Journal of the Econometric Society, 33-50.
8 Liu, Y. and Wu, Y. (2011). Simultaneous multiple non-crossing quantile regression estimation using kernel constraints, Journal of nonparametric statistics, 23, 415-437.   DOI
9 Loh, W. Y. (2002). Regression trees with unbiased variable selection and interaction detection, Statistica Sinica, 12, 361-386.
10 Luo, X., Huang, C. Y., and Wang, L. (2013). Quantile regression for recurrent gap time data, Biometrics, 69, 375-385.   DOI
11 Portnoy, S. (2003). Censored regression quantiles, Journal of the American Statistical Association, 98, 1001-1012.   DOI
12 Sun, X., Peng, L., Huang, Y., and Lai, H. J. (2016). Generalizing quantile regression for counting processes with applications to recurrent events, Journal of the American Statistical Association, 111, 145-156.   DOI
13 Theil, H. (1970). On the estimation of relationships involving qualitative variables, American Journal of Sociology, 76, 103-154.   DOI
14 Wang, H. J. and Fygenson, M. (2009). Inference for censored quantile regression models in longitudinal studies, The Annals of Statistics, 37, 756-781.   DOI