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Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using Max Stable Processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations. Supposed computationally-efficient approaches like the composite likelihood remain computationally burdensome with a few hundred observations. In this article, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers computationally and statistically efficient inference. Marginal and dependence parameters of the MSP are estimated locally on subsets of observations using censored pairwise composite likelihood, and combined using a modified generalized method of moments procedure. The proposed distributed approach is extended to estimate inverted MSP models, and to estimate spatially varying coefficient models to deliver computationally efficient modeling of spatial variation in marginal parameters. We demonstrate consistency and asymptotic normality of estimators, and show empirically that our approach leads to statistically efficient estimation of model parameters. We illustrate the flexibility and practicability of our approach through simulations and the analysis of streamflow data from the U.S. Geological Survey. for this article are available online. [ABSTRACT FROM AUTHOR]

The article discusses the reproducibility initiative at the Journal of the American Statistical Association (JASA). The initiative aims to address the lack of standardized practices for reproducibility in scientific research. It establishes minimum criteria for the inclusion of code, data, and workflow in research manuscripts and has implemented a new editorial role, the Associate Editor of Reproducibility (AER), to enforce these standards. The article explains the type of reproducibility being addressed, describes the steps of the review process, and provides guidelines for authors to streamline their reproducibility materials. The JASA reproducibility initiative is part of a larger movement in the scientific community to address the replicability crisis in science. The article emphasizes the importance of reproducibility in statistics to support the validity of research and foster transparency and accountability. It also addresses common questions and misconceptions about the reproducibility review process at JASA. The article concludes by encouraging wider discussions and the adoption of common standards for reproducibility in statistical journals. [Extracted from the article]

This article is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multilevel nested correlations. We develop a divide-and-conquer procedure implemented in a fully distributed and parallelized computational scheme for statistical estimation and inference of regression parameters. Despite significant efforts in the literature, the computational bottleneck associated with high-dimensional likelihoods prevents the scalability of existing methods. The proposed method addresses this challenge by dividing responses into subvectors to be analyzed separately and in parallel on a distributed platform using pairwise composite likelihood. Theoretical challenges related to combining results from dependent data are overcome in a statistically efficient way using a meta-estimator derived from Hansen's generalized method of moments. We provide a rigorous theoretical framework for efficient estimation, inference, and goodness-of-fit tests. We develop an R package for ease of implementation. We illustrate our method's performance with simulations and the analysis of the EEG data, and find that iron deficiency is significantly associated with two auditory recognition memory related potentials in the left parietal-occipital region of the brain. for this article are available online. [ABSTRACT FROM AUTHOR]