Search Results (4)

A critical aspect of modeling binomial response data is selecting an appropriate link function, as an improper choice can significantly affect model precision. This paper introduces the skew–t link model, an extension of the skew–probit model, offering increased flexibility by incorporating both asymmetry and heavy tails, making it suitable for asymmetric and complex data structures. A penalized likelihood-based estimation method is proposed to stabilize parameter estimation, particularly for the asymmetry parameter. Extensive simulation studies demonstrate the model’s superior performance in terms of lower bias, root mean squared error (RMSE), and robustness compared to traditional symmetric models like probit and logit. Furthermore, the model is applied to two real-world datasets: one concerning women’s labor participation and another related to cardiovascular disease outcomes, both showing superior fitting capabilities compared to more traditional models (with probit and the skew–probit links). These findings highlight the model’s applicability to socioeconomic and medical research, characterized by skew and asymmetric data. Moreover, the proposed model could be applied in various domains where data exhibit asymmetry and complex structures. Full article

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

Identifying the determinants of firms’ investment in knowledge, this study first explores the heterogeneous impacts of research and development (R&D) on product, process, organization, and marketing innovation. Second, it examines if there exists a complementary (substitute) relation in terms of firms’ preference between four types of innovation. Studying 1500 firms of seven developing economies of the Association of Southeast Asian Nations (ASEAN), we applied the least absolute shrinkage and selection operator (LASSO), a machine learning-based regression, to identify key predictors likely to influence firms’ R&D propensity and intensity. Estimating the knowledge function, we found—in line with LASSO—that medium-sized firms, human capital (training) and credit facilities favorably affect firms’ decision to invest in R&D. Contrarily, the impact is adverse if the first or main product generates firms’ large share of revenue, a unique finding not captured by previous studies. The marginal effects of four univariate probit models indicate that firms’ investment in R&D translates into innovation. However, the application of the Geweke–Hajivassiliour–Keane (GHK)-simulator based multivariate probit, which considers simultaneity of firms’ innovation decisions that univariate probit ignores, suggests that the relationship between different types of innovation is complementary. Firms’ strategy to adopt a particular type of innovation is influenced by other types. This led to the estimation of R&D’s impact on technological and nontechnological innovation, which shows that while firms innovate both types, there is a skewed link between nontechnological innovation and the services sector. Full article

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log–log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in detail. The analysis of two datasets to show the efficiency of the proposed model is performed. Full article