There is an increasing interest in the relationship between area-based disadvantage and obesity but the extent to which the poverty-obesity relationship remains constant across geographical areas remains unclear. We examined geographical variations in the relationship between poverty and obesity in Taiwan using geographically weighted regression (GWR). A representative sample of 27,293 Taiwanes...

In the linear regression setting, we propose a general framework, termed weighted orthogonal components (WOCR), which encompasses many known methods as special cases, including ridge and principal regression. WOCR makes use of monotonicity inherent in to parameterize weight function. The formulation allows for efficient determination tuning parameters hence is computationally advantageous. More...

COVID-19 incidence is analyzed at the provinces of some Spanish Communities during period February-October, 2020. Two infinite-dimensional regression approaches are tested. The first one implemented in framework introduced Ruiz-Medina, Miranda and Espejo (2019). Specifically, a bayesian adopted estimation pure point spectrum temporal autocorrelation operator, characterizing second-order structu...

This work studies the phenomenon of heteroscedasticity and its consequences for various methods of linear regression, including the least squares, least weighted squares and regression quantiles. We focus on hypothesis tests for these regression methods. The new approach consists in deriving asymptotic heteroscedasticity tests for robust regression, which are asymptotically equivalent to standa...

BACKGROUND AND OBJECTIVES Preterm and former preterm children frequently require sedation/anesthesia for diagnostic and therapeutic procedures. Our objective was to determine the age at which children who are born <37 weeks gestational age are no longer at increased risk for sedation/anesthesia adverse events. Our secondary objective was to describe the nature and incidence of adverse events. ...

In this paper we discuss implementing Bayesian fully nonparametric regression by defining a process prior on distributions which depend on covariates. We consider the problem of centring our process over a class of regression models and propose fully nonparametric regression models with flexible location structures. We also introduce a non-trivial extension of a dependent finite mixture model p...