<Para ID="Par1">In the framework of a random assignment process—which randomly assigns an index within a finite set of labels to the points of an arbitrary set—we study sufficient conditions for a strong law of large numbers and a De Finetti theorem. In particular, this yields a family of finite-valued...</para>

<Para ID="Par1">Remote sensing technology for the study of Earth and its environment has led to “Big Data” that, paradoxically, have global extent but may be spatially sparse. Furthermore, the variability in the measurement error and the latent process error may not fit conveniently into the Gaussian linear...</para>

<Para ID="Par1">Large spatial data sets require innovative techniques for computationally efficient statistical estimation. In this comment some aspects of local predictor selection are explored, with a view towards spatially coherent field prediction and uncertainty quantification. Copyright Sociedad de...</para>

<Para ID="Par1">In this paper, we present a method for estimating the conditional distribution function of the model error. Given the covariates, the conditional mean function is modeled as a partial linear model, and the conditional distribution function of model error is modeled as a single-index model. To...</para>

<Para ID="Par1">We motivate this paper by showing through Monte Carlo simulation that ignoring the skewness of the response variable distribution in non-linear regression models may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. Then, we propose a...</para>

<Para ID="Par1">In this paper, some basic properties for negatively superadditive-dependent (NSD, in short) random variables are presented, such as the Rosenthal-type inequality and the Kolmogorov-type exponential inequality. Using these properties, we further study the complete convergence for weighted sums of...</para>

<Para ID="Par1">This paper is concerned about robust comparison of two regression curves. Most of the procedures in the literature are least-squares-based methods with local polynomial approximation to nonparametric regression. However, the efficiency of these methods is adversely affected by outlying...</para>

<Para ID="Par1">We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved...</para>