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FUNCTIONAL CLUSTERING FOR LONGITUDINAL ASSOCIATIONS BETWEEN SOCIAL DETERMINANTS OF HEALTH AND STROKE MORTALITY IN THE U.S.

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Annals of applied statistics 2025年第1期19卷 798-820页

作者： Luo, Fangzhi Tan, Jianbin Zhang, Donglan Huang, Hui Shen, Ye Department of Epidemiology and Biostatistics College of Public Health University of Georgia United States Department of Biostatistics and Bioinformatics School of Medicine Duke University United States Department of Foundations of Medicine NYU Grossman Long Island School of Medicine United States Center for Applied Statistics and School of Statistics Renmin University of China China

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop datadriven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across U.S. counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity-and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of U.S. counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the U.S. counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights into region-specific SDOH adjustments for mitigating stroke mortality. © Institute of Mathematical statistics, 2025.

关键词： functional regression model Model-based clustering regularized expectation-maximization algorithm stroke mortality variable selection

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Spectral estimation for high-dimensional linear processes

arXiv

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arXiv 2025年

作者： Namdari, Jamshid Aue, Alexander Paul, Debashis Department of Biostatistics & Bioinformatics Emory University United States Department of Statistics University of California Davis United States Applied Statistics Unit Indian Statistical Institute Kolkata India

We propose a novel estimation procedure for certain spectral distributions associated with a class of high dimensional linear time series. The processes under consideration are of the form Xt = Í∞=0 AZt− with iid innovations (Zt). The key structural assumption is that the coefficient matrices and the variance of the innovations are simultaneously diagonalizable in a common orthonormal basis. We develop a strategy for estimating the joint spectral distribution of the coefficient matrices and the innovation variance by making use of the asymptotic behavior of the eigenvalues of appropriately weighted integrals of the sample periodogram. Throughout we work under the asymptotic regime p, n → ∞, such that p/n → c ∈ (0, ∞), where p is the dimension and n is the sample size. Under this setting, we first establish a weak limit for the empirical distribution of eigenvalues of the aforementioned integrated sample periodograms. This result is proved using techniques from random matrix theory, in particular the characterization of weak convergence by means of the Stieltjes transform of relevant distributions. We utilize this result to develop an estimator of the joint spectral distribution of the coefficient matrices, by minimizing an Lk discrepancy measure, for k ≥ 1, between the empirical and limiting Stieltjes transforms of the integrated sample periodograms. This is accomplished by assuming that the joint spectral distribution is a discrete mixture of point masses. We also prove consistency of the estimator corresponding to the L2 discrepancy measure. We illustrate the methodology through simulations and an application to stock price data from the S&P 500 series. © 2025, CC BY.

关键词： Asymptotic analysis

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Robust hierarchical co-clustering for exploring toxicogenomic biomarkers and their chemical regulators

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Scientific Reports 2025年第1期15卷 1-14页

作者： Mohammad Nazmol Hasan Md Bahadur Badsha Md. Nurul Haque Mollah Department of Statistics Gazipur Agricultural University Gazipur 1706 Bangladesh Center for Applied Bioinformatics St Jude Children’s Research Hospital Memphis TN USA Bioinformatics Lab. Department of Statistics University of Rajshahi Rajshahi 6205 Bangladesh

Toxicity measurement of doses of chemicals (DCs) is one of the most important tasks in toxicology studies and the drug discovery and development process. In this issue, toxicogenomic biomarkers are now playing a vital role in measuring the toxicity of DCs. Differentially expressed genes (DEGs) between DCs-treatment and control groups are considered toxicogenomic biomarkers, and associated chemicals are the regulators of DEGs. The co-clustering technique is now used extensively in toxicogenomic research to investigate co-clusters between genomic biomarkers and their chemical regulators. In the literature, there are few approaches to exploring co-clusters. The hierarchical co-clustering (HCoClust) approach is faster, simpler, and more flexible. Nevertheless, it is not robust against outlier data and there is no instruction about separating upregulatory or downregulatory co-clusters, a crucial goal of toxicogenomic data analysis. Therefore, in this article, we proposed a robust HCoClust (rHCoClust) approach and developed an r-package called “rhcoclust” for its implementation. Simulation results showed that the conventional HCoClust and the proposed rHCoClust performed equally well in detecting co-clusters in the absence of outliers, while rHCoClust performed much better than HCoClust in the presence of outliers. However, rHCoClust outperformed the bi-clustering approaches in detecting co-clusters, since bi-clustering methods only work when row and column clusters are equal, and they have no criterion for detecting upregulatory and downregulatory co-clusters. Then rHCoClust was compared with HCoClust through real data analysis and found that rHCoClust performed better than HCoClust. In the case of real data analysis, the proposed method rHCoClust identified top-ranked two DEGs-clusters (GSTA5, MGST2, GCLC, GCLM, G6PD) and (EHHADH, CYP4A1, ANGPT14, CPT1A) that were significantly expressed by the influence of top-ranked two DCs-clusters (acetaminophen_High _***, nitrofu

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On generalized fractal-fractional derivative and integral operators associated with generalized Mittag-Leffler function

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Heliyon 2025年第4期11卷 e42144页

作者： Khan, Hira Rahman, Gauhar Samraiz, Muhammad Shah, Kamal Abdeljawad, Thabet Department of Mathematics and Statistics Hazara University Mansehra 21300 Pakistan Department of Mathematics University of Sargodha P.O. Box 40100 Sargodha Pakistan Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai 602105 India Department of Medical Research China Medical University Taichung 40402 Taiwan Department of Mathematics and Applied Mathematics School of Science and Technology Sefako Makgatho Health Sciences University Ga-Rankuwa South Africa Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science and Technology Hawally 32093 Kuwait

In recent years, the Atangana-Baleanu (AB) fractal-fractional derivatives are widely used in many fields. In 2017, Atangana defined such operators by utilizing one parameter Mittag-Leffler function (M-L) function. Such operators have not yet been studied for three parameters M-L function. In this paper, we discuss further modifications of Caputo Fabrizio (CF), AB and generalized Hattaf fractal-fractional (GHF) operators. We used the modified three parameters M-L function to define the generalized fractal-fractional (GFF) differential and integral operators. We study an innovative class of new generalized weighted differential and integral operators. We define the generalized fractal-fractional (GFF) differential and integral operators with generalized Mittag-Leffler (M-L) kernels, which are used to simulate the complex dynamics of several natural and physical phenomena in a variety of scientific and engineering domains. There are a few established features of the newly defined operators. An example of an application for this new class of GFF integral is presented. Also, we discussed the graphical comparison of this new GFF operator with the existing GHF, AB and CF derivatives. Our case is the more general case compared with the existing fractal-fractional operators. We have presented some novel results for the new operators both analytically and graphically. Also, we discussed some special cases by giving specific value to the parameter θ. All the classical operators are restored by applying certain conditions on parameters. © 2025 The Authors

关键词： AB operators CF operators Fractal fractional derivatives Fractal fractional integrals Fractional derivative Fractional integral GHF operators Laplace transform Mittag-Leffler function

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Goodness-of-fit methods for probabilistic index models

Goodness-of-fit methods for probabilistic index models

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作者： De Neve, Jan Thas, Olivier Ottoy, Jean-Pierre Department of Mathematical Modelling Statistics and Bioinformatics Ghent University Coupure Links 653 B-9000 Gent Belgium Centre for Statistical and Survey Methodology School of Mathematics and Applied Statistics University of Wollongong NSW Australia

A class of semiparametric regression models, called probabilistic index models, has been recently proposed. Because these models are semiparametric, inference is only valid when the proposed model is consistent with the underlying data-generating model. However, no formal goodness-of-fit methods for these probabilistic index models exist yet. We propose a test and a graphical tool for assessing the model adequacy. Simulation results indicate that both methods succeed in detecting lack-of-fit. The methods are also illustrated on a case study. Copyright © Taylor & Francis Group, LLC.

关键词： statistics

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Challenges in biomarker discovery: Combining expert insights with statistical analysis of complex omics data

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Expert Opinion on Medical Diagnostics 2013年第1期7卷 37-51页

作者： McDermott, Jason E. Wang, Jing Mitchell, Hugh Webb-Robertson, Bobbie-Jo Hafen, Ryan Ramey, John Rodland, Karin D. Applied Statistics and Computational Modeling Pacific Northwest National Laboratory Computational Biology and Bioinformatics Richland WA 99352 902 Battelle Blvd. United States Biological Sciences for Computational Biology and Bioinformatics Pacific Northwest National Laboratory Computational Biology and Bioinformatics Richland WA 99352 902 Battelle Blvd. United States

Introduction: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers. © 2013 Informa UK, Ltd.

关键词： Biomarkers Classification Data integration Expert knowledge Molecular signatures

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Advancements in integral inequalities of Ostrowski type via modified Atangana-Baleanu fractional integral operator

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Heliyon 2025年第1期11卷 e41525页

作者： Rahman, Gauhar Samraiz, Muhammad Shah, Kamal Abdeljawad, Thabet Elmasry, Yasser Department of Mathematics and Statistics Hazara University Mansehra 21300 Pakistan Department of Mathematics University of Sargodha P.O. Box 40100 Sargodha Pakistan Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai 602105 India Department of Medical Research China Medical University Taichung 40402 Taiwan Department of Mathematics and Applied Mathematics School of Science and Technology Sefako Makgatho Health Sciences University Ga-Rankuwa South Africa Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science and Technology Hawally 32093 Kuwait Department of Mathematics College of Science King Khalid University P.O. Box 9004 Abha 61466 Saudi Arabia

Convexity and fractional integral operators are closely related due to their fascinating properties in the mathematical sciences. In this article, we first establish an identity for the modified Atangana-Baleanu (MAB) fractional integral operators. Using this identity, we then apply Jensen integral inequality, Young's inequality, power-mean inequality, and Hölder inequality to prove several new generalizations of Ostrowski type inequality for the convexity of |ℵ|. From the primary findings, we also deduced a few new special cases. The results of this investigation are expected to indicate new advances in the study of fractional integral inequalities. © 2024 The Author(s)

关键词： Convex function Fractional operators Power mean inequality Young inequality

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A zero-inflated model for spatiotemporal count data with extra zeros: application to 1950–2015 tornado data in Kansas

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Environmental and Ecological statistics 2024年第1期31卷 1-25页

作者： Yang, Hong-Ding Chang, Audrey Hsu, Wei-Wen Chen, Chun-Shu Department of Applied Mathematics National Chiayi University Chiayi Taiwan Department of Statistics Kansas State University Manhattan KS United States Division of Biostatistics and Bioinformatics Department of Environmental and Public Health Sciences University of Cincinnati Cincinnati OH United States Graduate Institute of Statistics National Central University Taoyüan Taiwan

In many tornado climate studies, the number of tornado touchdowns is often the primary outcome of interest. These outcome measures are usually generated under a spatiotemporal correlation structure and contains many zeros due to the rarity of tornado occurrence at a specific location and time interval. To model the spatiotemporal count data with excess zeros, we propose a spatiotemporal zero-inflated Poisson (ZIP) model, which lends itself to ease of interpretation and computational simplicity. Technically, we embed a modified conditional autoregressive model in the ZIP model to describe the spatial and temporal correlations, where the probability of a pure zero in the ZIP is purposely designed to depend on locations but independent of time. Illustrated with the longitudinal tornado touchdown data in the state of Kansas from 1950 to 2015, our model suggests that the spatial correlation among the counties and the corresponding temperature are significant factors attributed to the tornado touchdowns. Through the model, we can also estimate the probabilities of no tornado touchdowns for each county over time. These estimated probabilities substantially help us understand the pattern of touchdowns and further identify the risk areas across Kansas. Moreover, these estimates can be iteratively updated when more current touchdown data are available. The final model for Kansas tornado touchdown data is evaluated using more recent data. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Gibbs sampling Hierarchical Bayesian models Spatial conditional autoregressive Tornado data Zero inflation

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Flexible analysis of digital PCR experiments using generalized linear mixed models

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Biomolecular Detection and Quantification 2016年 9卷 1-13页

作者： Vynck, Matthijs Vandesompele, Jo Nijs, Nele Menten, Björn De Ganck, Ariane Thas, Olivier Department of Mathematical Modelling Statistics and Bioinformatics Ghent University Coupure Links 653 Ghent 9000 Belgium Center for Medical Genetics Ghent University De Pintelaan 185 Ghent 9000 Belgium Bioinformatics Institute Ghent N2N Ghent University De Pintelaan 185 Ghent 9000 Belgium Biogazelle Technologiepark 3 Zwijnaarde 9052 Belgium National Institute for Applied Statistics Research Australia (NIASRA) University of Wollongong 2522 NSW Australia

The use of digital PCR for quantification of nucleic acids is rapidly growing. A major drawback remains the lack of flexible data analysis tools. Published analysis approaches are either tailored to specific problem settings or fail to take into account sources of variability. We propose the generalized linear mixed models framework as a flexible tool for analyzing a wide range of experiments. We also introduce a method for estimating reference gene stability to improve accuracy and precision of copy number and relative expression estimates. We demonstrate the usefulness of the methodology on a complex experimental setup. © 2016 The Author(s).

关键词： Data analysis Digital PCR Mixed models Quantification Replicates statistics

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What can fuzzy cluster analysis contribute to clustering of high-dimensional data?

What can fuzzy cluster analysis contribute to clustering of ...

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10th International Workshop on Fuzzy Logic and Applications, WILF 2013

作者： Klawonn, Frank Bioinformatics and Statistics Helmholtz-Centre for Infection Research Inhoffenstr. 7 D-38124 Braunschweig Germany Department of Computer Science Ostfalia University of Applied Sciences Salzdahlumer Str. 46/48 D-38302 Wolfenbuettel Germany

ISBN: (纸本)9783319031996

Cluster analysis of high-dimensional data has become of special interest in recent years. The term high-dimensional data can refer to a larger number of attributes - 20 or more - as they often occur in database tables. But high-dimensional data can also mean that we have to deal with thousands of attributes as in the context of genomics or proteomics data where thousands of genes or proteins are measured and are considered in some analysis tasks as attributes. A main reason, why cluster analysis of high-dimensional data is different from clustering low-dimensional data, is the concentration of norm phenomenon, which states more or less that the relative differences between distances between randomly distributed points tend to be more and more similar in higher dimensions. On the one hand, fuzzy cluster analysis has been shown to be less sensitive to initialisation than, for instance, the classical k-means algorithm. On the other, standard fuzzy clustering is stronger affected by the concentration of norm phenomenon and tends to fail easily in high dimensions. Here we present a review of why fuzzy clustering has special problems with high-dimensional data and how this can be amended by modifying the fuzzifier concept. We also describe a recently introduced approach based on correlation and an attribute selection fuzzy clustering technique that can be applied when clusters can only be found in lower dimensions. © Springer International Publishing 2013.

关键词： Fuzzy clustering

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