Asymmetric versus Symmetric Binary Regresion: A New Proposal with Applications
نویسندگان
چکیده
The classical logit and probit models allow to explain a dichotomous dependent variable as function of factors or covariates which can influence the response variable. This paper introduces new skew-logit link for item theory by considering arctan transformation over scobit model, yielding very flexible from class generalized distribution. approach assumes an asymmetric reduces standard model special case parameters that control distribution’s symmetry. proposed is simple allows us estimate without using Bayesian methods, requires implementing Markov Chain Monte Carlo methods. Furthermore, no appears in formulation model. We compared with specification three datasets. first one deals well-known data collection widely studied statistical literature, concerning mortality adult beetle after exposure gaseous carbon disulphide, second considers automobile insurance portfolio. Finally, third dataset examines touristic related tourist expenditure. For these examples, results illustrate changes significance level some explanatory variables marginal effects. latter example, we have also modified definition intercept linear predictor prevent confounding.
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ژورنال
عنوان ژورنال: Symmetry
سال: 2022
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym14040733