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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 paradigm. In this paper, we consider the problem of selecting a predictor from a finite collection of spatial...
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.
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 NSD random variables, which generalizes and improves some corresponding ones for independent random variables...
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 estimate the single-index parameter, we propose a semi-parametric global weighted least-squares estimator...
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 heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the...
This rejoinder refers to the comments available at doi: 10.1007/s11749-014-0385-3 ; doi: 10.1007/s11749-014-0411-5 ; doi: 10.1007/s11749-014-0412-4 ; doi: 10.1007/s11749-014-0413-3 ; doi: 10.1007/s11749-014-0417-z .
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 nonexchangeable random variables that are conditionally independent given some other random variable, that is, they...
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 semiparametric regression model suitable for data set analysis in which the distribution of the response is...
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 observations and heavy-tailed distributions. To attack this challenge, a robust testing procedure is recommended...
In this paper the authors show how it is possible to establish a common structure for the exact distribution of the main likelihood ratio test (LRT) statistics used in the complex multivariate normal setting. In contrast to what happens when dealing with real random variables, for complex random variables it is shown that it is possible to obtain closed-form expressions for the exact distributions...
Inspired by nonlinear quantile regression, the article introduces, investigates, discusses, and illustrates a new concept of generalized elliptical location quantiles. They may require less stringent moment assumptions, be less sensitive to outliers, be less rigid, employ more a priori information regarding the location of the distribution, and have higher potential for various regression generalizations...
In recent years, there has been a growing interest in statistical methods for the analysis of spatially referenced data. The spatial dependence structure modeling is an indispensable tool to estimate the parameters that define this structure. In this paper, we use the family of elliptical distributions to estimate the spatial dependence in referenced data. Thus we extend the Gaussian spatial linear...
Let $$(X_n)$$ ( X n ) be a sequence of independent and identically distributed random variables, with common absolutely continuous distribution $$F$$ F . An observation $$X_n$$ X n is a near-record if $$X_n\in (M_{n-1}-a,M_{n-1}]$$ X n ∈ ( M n - 1 - a , M n - 1 ] , where $$M_{n}=\max \{X_1,\ldots ,X_{n}\}$$ M n = max { X 1 , … , X...
We are interested in the estimation and prediction of a parametric model on a short dataset upon which it is expected to overfit and perform badly. To overcome the lack of data (relatively to the dimension of the model), we propose the construction of an informative hierarchical Bayesian prior based on another longer dataset which is assumed to share some similarities with the original, short dataset...
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