In this article, we establish properties that relate quantiles of the log-skew-normal distribution to its parameters, allowing us to investigate the relationship between quantiles of a positive skewed response variable and a set of explanatory variables via the log-skew-normal linear regression model. We compute the maximum likelihood estimates of the parameters through a correspondence between the log-skew-normal and skew-normal linear regression models. Monte Carlo simulations show the satisfactory performance of the quantile estimators. An application to children’s data is presented and discussed.

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes.

El mar Caribe colombiano cuenta con reservas de hidrocarburos, diversidad ecosistémica, desarrollo de pesca, transporte marítimo, y el abastecimiento de redes de comunicación [1-3]. La región maneja una elevada demanda del espacio marino costero [2]. Por tanto, es necesario considerar un desarrollo sustentable y entender los riesgos físicos que implica, así como las dinámicas de los sistemas, y su afectación por las actividades antrópicas. El objetivo del trabajo es la identificación y análisis de elementos geológicos, climáticos y oceanográficos que se encuentran en zonas profundas y su incidencia sobres el territorio. El impacto de los elementos naturales, fue cuantificado mediante el índice ICI (Índice de Cuantificación de Impacto) propuesto en este trabajo y que resume diferentes fuentes de variación en un único valor. Se presenta un análisis estadístico de interacciones cualitativas para la comprensión de las problemáticas ambientales desde una visión biofísica y la generación de soluciones eficaces a las mismas.

We propose and empirically evaluate a two-stage approximation to a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the joint bias correction of five NASA POWER reanalysis variables: minimum and maximum temperature ($T_{\min }$, $T_{\max }$), relative humidity (RH), solar radiation (Rad), and precipitation occurrence ($P_{\mathrm{bin}}$). Our primary contribution is the first operational-scale evaluation of such a framework (≈200,000 station–day observations, two orders of magnitude beyond previous studies) together with its deployment in an open-access web application. A systematic grid of more than 200 marginal configurations is evaluated on a strict chronological 70/30 hold-out (training 2016–2022; testing 2023–2025) to identify the optimal marginal specification per variable. Against a correctly specified marginal baseline, station-level linear calibration combined with the marginal GAMM removes the bulk of the systematic bias (RMSE reductions of ≈80%, 82% and 30% for $T_{\min }$, $T_{\max }$ and RH). A shared latent step, using the first principal component of the marginal residual matrix as a scalar proxy for ${\Lambda }_0\left(t\right)$, yields additional but conditional out-of-sample reductions (≈17% $T_{\max }$, 10% RH, 9% Rad; negligible for $T_{\min }$, with precipitation occurrence retained in the shared representation but its joint gain treated as exploratory); because it requires co-located donor observations, at ungauged locations the deployed pipeline applies the marginal correction only, whose spatial transfer is confirmed by leave-one-station-out cross-validation. The residual cross-correlation structure is consistent with, though not in itself proof of, Clausius–Clapeyron coupling. The trained artefacts are deployed in ColClim, an open-access R Shiny application that queries the NASA POWER API and the Open-Meteo forecast service for any location in Colombia and delivers historical bias-corrected series and short-range (1–16 day) forecasts.