The aim of this dissertation is to study a family of asymmetric link functions for binary regression models under Bayesian approach. Specifically, we present the estimation of parameters of power and reversal power binary ...

This work consider the problem of how to incorporate model selection uncertainty into statistical inference, through model averaging, applied to logistic regression. It will be used the approach of Buckland et. al. (1997), ...

The objective of this work is to study the binary regression model under the frequentist and Bayesian approaches using the probit, logit, log-log complement, Box-Cox transformation and skewprobit as link functions. In the ...

In binary regression, imbalanced data result from the presence of values equal to zero (or one) in a proportion that is significantly greater than the corresponding real values of one (or zero). In this work, we evaluate ...

Maximum likelihood estimators for the logistic regression model with misclassification in the response variable are extremely biased when error probabilities are ignored. If misclassification parameters are incorporated ...

Studies involving binary responses with hierarchical clustering are often found in different areas of knowledge. In regression models clustering structures should be properly handled, once they violate basic assumption of ...

Studies involving binary responses with hierarchical clustering are often found in different areas of knowledge. In regression models clustering structures should be properly handled, once they violate basic assumption of ...