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31st International Conference on Machine Learning, ICML 2014

作者： Azizi, Elham Airoldi, Edoardo M. Galagan, James E. Bioinformatics Program Boston University BostonMA02215 United States Department of Statistics Harvard University CamrbdigeMA02138 United States Departments of Biomedical Engineering and Microbiology Boston University BostonMA02215 United States

ISBN: (纸本)9781634393973

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individ-uals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and un- verifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible- jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn regulatory modules in the Mycobacterium tuberculosis gene regulatory network. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Probability distributions

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Low-rank variational Bayes correction to the Laplace method

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3032-3056页

作者： Janet Van Niekerk Håvard Rue Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

Approximate inference methods like the Laplace method, Laplace approximations and variational methods, amongst others, are popular methods when exact inference is not feasible due to the complexity of the model or the abundance of data. In this paper we propose a hybrid approximate method called Low-Rank Variational Bayes correction (VBC), that uses the Laplace method and subsequently a Variational Bayes correction in a lower dimension, to the joint posterior mean. The cost is essentially that of the Laplace method which ensures scalability of the method, in both model complexity and data size. Models with fixed and unknown hyperparameters are considered, for simulated and real examples, for small and large data sets.

关键词： information processing rule INLA Laplace LGM variational Bayes

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Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

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Information Sciences 2024年 670卷

作者： de Andrade, Ricardo Lopes Rêgo, Leandro Chaves Management Engineering Graduate Program Federal University of Pernambuco Brazil Statistics and Applied Math Department Universidade Federal do Ceará Brazil

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems. © 2024 Elsevier Inc.

关键词： Commerce

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STATISTICAL INFERENCE FOR GAUSSIAN WHITTLE-MATÉRN FIELDS ON METRIC GRAPHS

arXiv

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arXiv 2023年

作者： Bolin, David Simas, Alexandre B. Wallin, Jonas Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Statistics Lund University Sweden

Whittle-Matérn fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equation. Unlike earlier covariance-based approaches for specifying Gaussian fields on metric graphs, the Whittle-Matérn fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties of the model class, particularly the consistency and asymptotic normality of maximum likelihood estimators of model parameters and the necessary and sufficient conditions for asymptotic optimality properties of linear prediction based on the model with misspecified parameters. The covariance function of the Whittle-Matérn fields is generally unavailable in closed form, and they have therefore been challenging to use for statistical inference. However, we show that for specific values of the fractional exponent, when the fields have Markov properties, likelihood-based inference and spatial prediction can be performed exactly and computationally efficiently. This facilitates using the Whittle-Matérn fields in statistical applications involving big datasets without the need for any approximations. The methods are illustrated via an application to modeling of traffic data, where allowing for differentiable processes dramatically improves the *** Codes Primary 62M30, secondary 62M05, 60G60, 35R02 Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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The Influence of the Model Fractal Parameters on the Electromagnetic Response in Environment with Cylindrical Layers

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International Journal of Geosciences 2017年第3期8卷 349-363页

作者： Valcir Joã o da Cunha Farias Brígida Ramati Pereira da Rocha Alice Nabiç a Mores Faculty of Statistics Federal University of Pará Belém Brazil Pos-Graduate Program in Electrical Engineering Federal University of Pará Belém Brazil

The induced polarization response of an environment presenting cylindrical layers was obtained. The fractal model for complex resistivity was employed as an intrinsic property of the polarizable layers. The influence of the model fractal parameters on the electromagnetic response was investigated. The results demonstrated that the fractal parameters dominate the apparent resistivity phase response;measurements of the induced polarization data allow for the determination of the fractal properties of the environment without noticeable electromagnetic coupling effects at frequencies below 104 Hz.

关键词： Fractal Model Induced Polarization Complex Resistivity Well Log

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A bootstrap test for equality of variances

arXiv

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arXiv 2018年

作者： Cahoy, Dexter O. Program of Mathematics and Statistics College of Engineering and Science Louisiana Tech University RustonLA71272 United States

We introduce a bootstrap procedure to test the hypothesis Ho that K +1 variances are homogeneous. The procedure uses a variance-based statistic, and is derived from a normal-theory test for equality of variances. The test equivalently expressed the hypothesis as Ho : η = (η1, . . ., ηK+1)T = 0, where ηi’s are log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis Ho. Simulation results indicated that our method is generally superior to the Shoemaker and Levene tests, and the bootstrapped version of Levene test in controlling the Type I and Type II errors. Copyright © 2018, The Authors. All rights reserved.

关键词： Acceptance tests

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KenCoh: A Ranked-Based Canonical Coherence 1

arXiv

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arXiv 2024年

作者： Talento, Mara Sherlin D.P. Roy, Sarbojit Ombao, Hernando Statistics Program Computer Electrical and Mathematical Science & Engineering King Abdullah University of Science and Technology Saudi Arabia

In this paper, we consider the problem of characterizing a robust global dependence between two brain regions where each region may contain several voxels or channels. This work is driven by experiments to investigate the dependence between two cortical regions and to identify differences in brain networks between brain states, e.g., alert and drowsy states. The most common approach to explore dependence between two groups of variables (or signals) is via canonical correlation analysis (CCA). However, it is limited to only capturing linear associations and is sensitive to outlier observations. These limitations are crucial because brain network connectivity is likely to be more complex than linear and that brain signals may exhibit heavy-tailed properties. To overcome these limitations, we develop a robust method, Kendall canonical coherence (KenCoh), for learning monotonic connectivity structure among neuronal signals filtered at given frequency bands. Furthermore, we propose the KenCoh-based permutation test to investigate the differences in brain network connectivity between two different states. Our simulation study demonstrates that KenCoh is competitive to the traditional variance-covariance estimator and outperforms the later when the underlying distributions are heavy-tailed. We apply our method to EEG recordings from a virtual-reality driving experiment. Our proposed method led to further insights on the differences of frontal-parietal cross-dependence network when the subject is alert and when the subject is drowsy and that left-parietal channel drives this dependence at the beta-band. © 2024, CC BY.

关键词： statistics

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High-dimensional data fusion via joint manifold learning

High-dimensional data fusion via joint manifold learning

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2010 AAAI Fall Symposium

作者： Davenport, Mark A. Hegde, Chinmay Duarte, Marco F. Baraniuk, Richard G. Department of Statistics Stanford University Stanford CA 94305 United States Department of Electrical and Computer Engineering Rice University Houston TX 77005 United States Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 United States

ISBN: (纸本)9781577354888

The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of data governed by a low-dimensional set of ***, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for manifold learning. Additionally, we leverage recent results concerning random projections of manifolds to formulate a universal, network-scalable dimensionality reduction scheme that efficiently fuses the data from all sensors. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Sensor networks

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Simulating weed scouting and weed control decision making to evaluate scouting plans 92

Simulating weed scouting and weed control decision making to...

引用

24th Conference on Winter Simulation, WSC 1992

作者： Wiles, Lori J. Wilkerson, Gail G. Gold, Harvey J. Agricultural Engineering Research Center USDA-ARS Colorado State University Fort CollinsCO80523 United States Department of Crop Science Biomsthematics Program Department of Statistics North Carolina State University RaleighNC27695 United States

ISBN: (纸本)0780307984

The weed seedling population in a field must be scouted or sampled to choose the most appropriate postemergence control treatment. The cost-effectiveness of a scouting plan must be evaluated to confidently recommend its use. We conducted simulation experiments to evaluate scouting plans for use with a microcomputer postemergence weed control decision model for soybeans. The following were simulated the process of scouting, use of the decision model with the scouting information, and the resulting profit from the decision model's recommendation. Simulations were based on &ta from 14 North Carolina soybean fields. While scouting is recognized as valuable for determining if control is required, our results highlight the value of scouting for choosing among treatments when the need for control is obvious. Our results also indicate that the scouting plan recommended for use with the decision model is cost-effective. However, some risk averse decision makers may wish to scout more intensively. Use of simulation to evaluate weed scouting plans is currently constrained by the lack of &ta on the cost of scouting and the distribution of weeds within fields. © 1992 ACM.

关键词： Cost effectiveness

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Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics

Predictive Modeling of Sports-Related Concussions using Clin...

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2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

作者： Subhash, Sujit Obafemi-Ajayi, Tayo Goodman, Dennis Wunsch Ii, Donald Olbricht, Gayla R. Missouri University of Science Technology Mathematics and Statistics Dept RollaMO United States Missouri State University Engineering Program SpringfieldMO United States Missouri University of Science Technology Director of Sports Medicine RollaMO United States

ISBN: (纸本)9781728125473

Concussions represent a growing health concern that are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. While research in machine learning applications for concussions has focused on using advanced metrics such neuroimaging techniques and blood biomarkers, these metrics are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process reliant on a physician's interpretation of these assessment scores. This study aims to explore the use of machine learning techniques to aid the concussion diagnosis process. These models could provide an automated means to flag concussed patients even before being seen by a doctor as well as expand the scope of concussion diagnosis to remote locations and areas with limited access to doctors. © 2020 IEEE.

关键词： Diagnosis

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