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Item type:Publication, A robust regression model for bounded count health data(SAGE Publications Ltd, 2024-08-01)Bounded count response data arise naturally in health applications. In general, the well-known beta-binomial regression model form the basis for analyzing this data, specially when we have overdispersed data. Little attention, however, has been given to the literature on the possibility of having extreme observations and overdispersed data. We propose in this work an extension of the beta-binomial regression model, named the beta-2-binomial regression model, which provides a rather flexible approach for fitting a regression model with a wide spectrum of bounded count response data sets under the presence of overdispersion, outliers, or excess of extreme observations. This distribution possesses more skewness and kurtosis than the beta-binomial model but preserves the same mean and variance form of the beta-binomial model. Additional properties of the beta-2-binomial distribution are derived including its behavior on the limits of its parametric space. A penalized maximum likelihood approach is considered to estimate parameters of this model and a residual analysis is included to assess departures from model assumptions as well as to detect outlier observations. Simulation studies, considering the robustness to outliers, are presented confirming that the beta-2-binomial regression model is a better robust alternative, in comparison with the binomial and beta-binomial regression models. We also found that the beta-2-binomial regression model outperformed the binomial and beta-binomial regression models in our applications of predicting liver cancer development in mice and the number of inappropriate days a patient spent in a hospital.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parametric evaluation of the response modification factor R considering bidirectional ground motions(Elsevier Ltd, 2024-11-01)The current research on evaluating the response modification factor R, related to the lateral strength demand of structures, has been generally based on inelastic single-degree-of-freedom systems. Nevertheless, most structures have more than one main analysis component and will be subjected to bidirectional ground motions. In this study, the results of the parametric evaluation of the response modification factor considering the bidirectional interaction (Rb) of inelastic two-degree-of-freedom systems (2DOF) are presented. The effects on the factor Rb of the vibration period, the ductility capacity, the hysteretic model, the seismic incidence angle, and the period ratio were evaluated. Analysis results show that the bidirectional interaction could increase the lateral strength demand of the 2DOF systems because of the coupling effect of the two components’ responses. To make this research useful for improving engineering practice and code provisions, the main contribution is the proposal of a simplified expression for estimating the factor Rb. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A new quantile regression model for bounded responses with applications(Springer Science+Business Media, 2025-11-01)This work proposes a new quantile regression model with a bounded response distribution that generalizes the L-logistic distribution. Following a Bayesian approach, estimation model comparison criteria and residual analysis are performed as well as a simulation study for prior sensitivity and parameter recovery, considering a computationally intensive approach. An application of the new distribution to model poverty vulnerability in Brazil and a regression analysis with poverty data from Peru is included. Comparison with the Beta and L-Logistic distributions are also performed showing the great flexibility of the new model.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A New Class of Binary Regression Models for Unbalanced Data with Applications in Medical Data(Springer Science+Business Media, 2025-08-01)Imbalanced binary data may be more common than expected in medical trials. In this paper, we propose a new class of link function for binary response based on the cumulative distribution function of the scale mixture of skew-normal distributions, which can be useful for fitting imbalanced binary data. The proposed link class has as special cases several link functions proposed in the literature, such as the probit and Student’s-t link, and we present a Bayesian approach for model fitting. Further, we develop Bayesian case-deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated with one example as well as a simulation, which illustrates the potential of the proposed class of links as an alternative for binary regression models when imbalanced binary data is presented.1