{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:28:31Z","timestamp":1740112111151,"version":"3.37.3"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"Supp01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:p> In many practical situations, it is desirable to predict binary (\u201cyes\u201d\u2013\u201cno\u201d) decisions made by people. The traditional approach to this prediction assumes that the utility linearly depends on the corresponding parameters, and that the distribution of the difference between predicted and actual utility is symmetric \u2014 usually normal or logistic; the corresponding techniques are known as, correspondingly, probit and logit. In real life, utility often non-linearly depends on the parameters, and the corresponding distributions are asymmetric (skewed). There are techniques for dealing with non-linearity; the most widely used such technique \u2014 called kink regression \u2014 uses piece-wise linear approximations to the utility. There are also techniques that take into account the distribution\u2019s asymmetry; usually, they are based on using special asymmetric distributions: skew-normal and skew-logistic. In this paper, we show how these two techniques to be combined to take into account both non-linearity and asymmetry. On a real-life example, we show that the new technique indeed leads to a better description of human binary decision-making. <\/jats:p>","DOI":"10.1142\/s0218488520400048","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T01:50:07Z","timestamp":1596505807000},"page":"39-49","source":"Crossref","is-referenced-by-count":1,"title":["How to Take Both Non-Linearity and Asymmetry (Skewness) into Account in Binary Decision Making: Skew-Probit and Skew-Logit in Binary Kink Regression"],"prefix":"10.1142","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3794-6161","authenticated-orcid":false,"given":"Paravee","family":"Maneejuk","sequence":"first","affiliation":[{"name":"Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,8,28]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.12988\/imf.2019.9834"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/83.4.715"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1080\/03610920902783849"},{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1080\/0266476022000006694"},{"key":"p_8","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473872"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476321"},{"issue":"2","key":"p_11","first-page":"113","volume":"11","author":"Flores Muiz J. 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