arXiv subjects

Francisco F. Queiroz

Publications and source records attributed to Francisco F. Queiroz.

4 recordsLinked to original sources

arXiv2026

Comprehensive Regression and Diagnostics for Non-Negative Data Using the BCSreg Package

Continuous positive data characterized by high skewness and heavy tails frequently arise in applied statistics. In other applications, these characteristics are accompanied by a point mass at zero, resulting in a non-negative response with a mixed discrete-continuous distribution. Standard regression models often fail to capture these complex features adequately, requiring more flexible approaches. In this paper, we introduce the BCSreg package for R, which provides a comprehensive and unified computational framework for fitting Box-Cox symmetric and log-symmetric regression models for positive continuous data and their zero-adjusted extensions for mixed non-negative data. These broad classes of models accommodate varying degrees of skewness and tail-heaviness while allowing the parameters to be interpreted directly on the original scale of the data. Through a user-friendly multi-part formula interface, the BCSreg package allows practitioners to simultaneously specify regression structures for the scale parameter (which is proportional to the quantiles of the response), the relative dispersion, and, when appropriate, the probability of zero occurrences. Furthermore, the package provides a complete suite of diagnostic tools specifically tailored to these classes of models, including randomized quantile residuals, simulated envelopes, and influence diagnostics. The package's features and capabilities are illustrated through applications to real data.

stat.CO↗

arXiv2026

Bayesian structured additive quantile regression for inflated bounded data

Bounded continuous data on the unit interval frequently arise in applied fields and often exhibit a non-negligible proportion of observations at the boundaries. Inflated regression models address this feature by combining a continuous distribution on the unit interval with a discrete component to account for zero- and/or one-inflation. In this paper, we propose a class of Bayesian structured additive quantile regression models for inflated bounded continuous data that accommodates zero- and/or one-inflation. The proposed approach enables direct modeling of both the conditional quantiles of the continuous component and the probabilities of observing zeros and/or ones, with structured additive predictors incorporated in both parts, including nonlinear effects, spatial effects, random effects, and varying-coefficient terms. Posterior inference is carried out using Markov chain Monte Carlo algorithms implemented through the software Liesel, a probabilistic programming framework for semiparametric regression. The practical performance of the proposed models is illustrated through simulation studies and two real-data applications: one analyzing the proportion of traffic-related fatalities across Brazilian municipal districts, and another evaluating speech intelligibility in cochlear implant recipients under different experimental conditions.

stat.ME↗

arXiv2026

Flexible modeling of nonnegative continuous data: Box-Cox symmetric regression and its zero-adjusted extension

The Box-Cox symmetric distributions constitute a broad class of probability models for positive continuous data, offering flexibility in modeling skewness and tail behavior. Their parameterization allows a straightforward quantile-based interpretation, which is particularly useful in regression modeling. Despite their potential, only a few specific distributions within this class have been explored in regression contexts, and zero-adjusted extensions have not yet been formally addressed in the literature. This paper formalizes the class of Box-Cox symmetric regression models and introduces a new zero-adjusted extension suitable for modeling data with a non-negligible proportion of observations equal to zero. We discuss maximum likelihood estimation, assess finite-sample performance through simulations, and develop diagnostic tools including residual analysis, local influence measures, and goodness-of-fit statistics. An empirical application on basic education expenditure illustrates the models' ability to capture complex patterns in zero-inflated and highly skewed nonnegative data. To support practical use, we developed the new BCSreg R package, which implements all proposed methods.

stat.ME↗

arXiv2022

Robust beta regression through the logit transformation

Beta regression models are employed to model continuous response variables in the unit interval, like rates, percentages, or proportions. Their applications rise in several areas, such as medicine, environment research, finance, and natural sciences. The maximum likelihood estimation is widely used to make inferences for the parameters. Nonetheless, it is well-known that the maximum likelihood-based inference suffers from the lack of robustness in the presence of outliers. Such a case can bring severe bias and misleading conclusions. Recently, robust estimators for beta regression models were presented in the literature. However, these estimators require non-trivial restrictions in the parameter space, which limit their application. This paper develops new robust estimators that overcome this drawback. Their asymptotic and robustness properties are studied, and robust Wald-type tests are introduced. Simulation results evidence the merits of the new robust estimators. Inference and diagnostics using the new estimators are illustrated in an application to health insurance coverage data.

stat.ME↗