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Motivated by image‐on‐scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of a joint analysis of these data sources, we must learn which of them share mean model parameters. We propose a new model fusion approach that delivers improved flexibility and statistical performance over existing methods. Our proposed approach specifies a quadratic inference function within each data source and fuses mean model parameter vectors in their entirety based on a new formulation of a pairwise fusion penalty. We establish theoretical properties of our estimator and propose an asymptotically equivalent weighted oracle meta‐estimator that is more computationally efficient. Simulations and an application to the ABIDE neuroimaging consortium highlight the flexibility of the proposed approach. An R package is provided for ease of implementation. [ABSTRACT FROM AUTHOR]

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]

Rationale: Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix‐assisted laser desorption/ionization [MALDI], MALDI‐2, and infrared matrix‐assisted laser desorption electrospray ionization [IR‐MALDESI]). Methods: A novel QC mix was sprayed across glass slides to collect QC/SST regions‐of‐interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary. Results: The PCA‐based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR‐MALDESI‐MSI analyses. This minimizes the type‐I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning. Conclusions: This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms. [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]