检索结果-内蒙古大学图书馆

Comparison of some tests of fit for the inverse gaussian distribution

学校读者我要写书评

暂无评论

Advances in Decision Sciences 2012年第unknown期2012卷 150303.1-150303.9页

作者： Best, D.J. Rayner, J.C.W. Thas, O. School of Mathematical and Physical Sciences University of Newcastle NSW 2308 Australia Centre for Statistical and Survey Methodology School of Mathematics and Applied Statistics University of Wollongong NSW 2522 Australia Department of Mathematical Modelling Statistics and Bioinformatics 9000 Gent Belgium

关键词： Gaussian distribution Inverse goodness of fit

Local two-sample testing over graphs and point-clouds by random-walk distributions

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Landa, Boris Qu, Rihao Chang, Joseph Kluger, Yuval Program in Applied Mathematics Yale University Department of Pathology Yale University School of Medicine Department of Statistics and Data Science Yale University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Immunology Yale School of Medicine

Rejecting the null hypothesis in two-sample testing is a fundamental tool for scientific discovery. Yet, aside from concluding that two samples do not come from the same probability distribution, it is often of interest to characterize how the two distributions differ. Given samples from two densities f1and f0, we consider the task of localizing occurrences of the inequality f1> f0. To avoid the challenges associated with high-dimensional space, we propose a general hypothesis testing framework where hypotheses are formulated adaptively to the data by conditioning on the combined sample from the two densities. We then investigate a special case of this framework where the notion of locality is captured by a random walk on a weighted graph constructed over this combined sample. We derive a tractable testing procedure for this case employing a type of scan statistic, and provide non-asymptotic lower bounds on the power and accuracy of our test to detect whether f1> f0in a local sense. Furthermore, we characterize the test's consistency according to a certain problem-hardness parameter, and show that our test achieves the minimax detection rate for this parameter. We conduct numerical experiments to validate our method, and demonstrate our approach on two real-world applications: detecting and localizing arsenic well contamination across the United States, and analyzing two-sample single-cell RNA sequencing data from melanoma *** Codes 62G10 Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

Toward Resilient Multi-Agent Actor-Critic Algorithms for Distributed Reinforcement Learning

学校读者我要写书评

暂无评论

Toward Resilient Multi-Agent Actor-Critic Algorithms for Dis...

American Control Conference

作者： Yixuan Lin Shripad Gade Romeil Sandhu Ji Liu Department of Applied Mathematics and Statistics Stony Brook University Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Departments of Bioinformatics and Computer Science Stony Brook University Department of Electrical and Computer Engineering Stony Brook University

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper considers a distributed reinforcement learning problem in the presence of Byzantine agents. The system consists of a central coordinating authority called "master agent" and multiple computational entities called "worker agents". The master agent is assumed to be reliable, while, a small fraction of the workers can be Byzantine (malicious) adversaries. The workers are interested in cooperatively maximize a convex combination of the honest (non-malicious) worker agents' long-term returns through communication between the master agent and worker agents. A distributed actor-critic algorithm is studied which makes use of entry-wise trimmed mean. The algorithm's communication-efficiency is improved by allowing the worker agents to send only a scalar-valued variable to the master agent, instead of the entire parameter vector, at each iteration. The improved algorithm involves computing a trimmed mean over only the received scalar-valued variable. It is shown that both algorithms converge almost surely.

关键词： Agents algorithms Workers Learning Distribute Personnel

Bayesian prediction via nonparametric transformation models

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhong, Chong Yang, Jin Shen, Junshan Liu, Catherine C. Li, Zhaohai Biostatistics and Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development NIH United States School of Statistics Capital University of Economics and Business China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Statistics George Washington University WashingtonDC United States

This article tackles the old problem of prediction via a nonparametric transformation model (NTM) in a new Bayesian way. Estimation of NTMs is known challenging due to model unidentifiability though appealing because of its robust prediction capability in survival analysis. Inspired by the uniqueness of the posterior predictive distribution, we achieve efficient prediction via the NTM aforementioned under the Bayesian paradigm. Our strategy is to assign weakly informative priors to nonparametric components rather than identify the model by adding complicated constraints in existing literature. The Bayesian success pays tribute to i) a subtle cast of NTMs by an exponential transformation for the purpose of compressing spaces of infinite-dimensional parameters to positive quadrants considering non-negativity of the failure time;ii) a newly constructed weakly informative quantile-knots I-splines prior for the recast transformation function together with the Dirichlet process mixture model assigned to the error distribution. In addition, we provide a convenient and precise estimator for the identified parameter component subject to the general unit-norm restriction through posterior modification, enabling effective relative risks. Simulations and applications on real datasets reveal that our method is robust and outperforms the competing methods. An R package BuLTM is available to predict survival curves, estimate relative risks, and facilitate posterior checking. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Case-centred multidimensional scaling for classification visualisation in medical diagnosis

学校读者我要写书评

暂无评论

Case-centred multidimensional scaling for classification vis...

2nd International Conference on Health Information Science, HIS 2013

作者： Klawonn, Frank Lechner, Werner Grigull, Lorenz Department of Computer Science Ostfalia University of Applied Sciences Salzdahlumer Str. 46/48 D-38302 Wolfenbuettel Germany Bioinformatics and Statistics Helmholtz Centre for Infection Research Inhoffenstr. 7 D-38124 Braunschweig Germany Improved Medical Diagnostics Pte Ltd. 19-05 Fortune Centre 190 Middle Road Singapore 188979 Singapore Department of Pediatric Haematology and Oncology Medical University Carl-Neubergstr. 1 Hannover Germany

ISBN: (纸本)9783642378980

Computer-based decision support can assist a medical doctor to find the right diagnosis. The knowledge and experience of the medical doctor is enhanced by a much larger data set of patients than the doctor will ever see in her or his life. The decision support system can derive possible diagnoses for a new patient based on a suitable classifier built on the patients in the patient database. However, since such a system cannot replace a medical doctor and should only support her or him, it should also provide information about the certainty of its recommendation. In this paper, we propose to visualise how close or similar the new patient is to others in the database by a modified multidimensional scaling technique that focuses on the correct positioning of the new patient in the visualisation. In this way, the medical doctor can easily see whether the diagnosis recommended by the system is reliable when all patients close to the new patient have the same diagnosis or whether it is quite uncertain when the new patient is surrounded by patients with different diagnoses. © 2013 Springer-Verlag.

关键词： Visualization

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2:5 concentration

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Berrocal, Veronica J. Guan, Yawen Muyskens, Amanda Wang, Haoyu Reich, Brian J. Mulholland, James A. Chang, Howard H. Department of Biostatistics School of Public Health University of Michigan Department of Statistics North Carolina State University Statistical and Applied Mathematical Sciences Institute Environmental Engineering Georgia Tech Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from characterizing ambient air pollution exposure solely using measurements. In the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at finer spatial and temporal scales (daily, usually) with complete coverage. Some of these methods include: geostatistical techniques, such as kriging;spatial statistical models that use the information contained in air quality model outputs (statistical downscaling models);linear regression modeling approaches that leverage the information in GIS covariates (land use regression);or machine learning methods that mine the information contained in relevant variables (neural network and deep learning approaches). Although some of these exposure modeling approaches have been used in several air pollution epidemiological studies, it is not clear how much the predicted exposures generated by these methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance and computational difficulty. Using PM2.5in year 2011 over the continental U.S. as case study, we examine the methods' performances across seasons, rural vs urban settings, and levels of PM2.5concentrations (low, medium, high). Copyright © 2019, The Authors. All rights reserved.

关键词： Kriging

Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling

学校读者我要写书评

暂无评论

Nature Communications 2025年第1期16卷 1-17页

作者： Jasper Anckaert Francisco Avila Cobos Anneleen Decock Philippe Decruyenaere Jill Deleu Jilke De Wilde Thibaut D’huyvetter Celine Everaert Hetty H. Helsmoortel Eva Hulstaert Pieter Mestdagh Annelien Morlion Justine Nuytens Franco Poma-Soto Kathleen Schoofs Eveline Vanden Eynde Jo Vandesompele Tom Van Maerken Ruben Van Paemel Kimberly Verniers Jasper Verwilt Nurten Yigit Katleen De Preter Olivier De Wever Bert Dhondt An Hendrix Annouck Philippron Carolina Fierro Nele Nijs Thomas Piofczyk Jan Koster Scott Kuersten Gary P. Schroth Tim R. Mercer Olivier Thas OncoRNALab Department of Biomolecular Medicine Ghent University Ghent Belgium Cancer Research Institute Ghent (CRIG) Ghent Belgium TOBI lab Center for Medical Genetics Department of Biomolecular Medicine Ghent University Ghent Belgium Department of Hematology Ghent University Hospital Ghent Belgium Department of Dermatology Ghent University Hospital Ghent Belgium Biogazelle Zwijnaarde Belgium Department of Laboratory Medicine AZ Groeninge Kortrijk Belgium Department of Pediatrics Ghent University Hospital Ghent Belgium Laboratory of Experimental Cancer Research Ghent University Ghent Belgium Department of Urology Ghent University Hospital Ghent Belgium Lab for Experimental Surgery Ghent University Ghent Belgium Department of Gastrointestinal Surgery Ghent University Hospital Ghent Belgium Center for Experimental and Molecular Medicine (CEMM) Amsterdam University Medical Centers University of Amsterdam Amsterdam The Netherlands Illumina San Diego California USA Australian Institute of Bioengineering and Nanotechnology University of Queensland Brisbane QLD Australia Kinghorn Centre for Clinical Genomics Garvan Institute of Medical Research Sydney NSW Australia Data Science Institute (DSI) Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) Hasselt University Hasselt Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Gent Belgium National Institute of Applied Statistics Research Australia (NIASRA) University of Wollongong Wollongong Australia

Blood-based extracellular RNA (cell-free RNA; exRNA) biomarkers require validated sample collection, processing, and quantification procedures. No study to date has systematically tested pre-analytical variables affecting transcriptome-wide exRNA analysis. By evaluating their impact on deep transcriptome profiling of microRNAs and mRNAs in blood plasma or serum, we compared ten blood collection tubes, three blood processing time intervals, and eight RNA purification methods. In addition, we assessed interactions among a selected pre-analytical variable set, resulting in 456 extracellular transcriptomes. Blood preservation tubes failed to stabilize exRNA and RNA purification methods differed significantly in performance, causing variations in concentration, detected gene numbers, replicability and observed transcriptome complexity. Critical interactions between tubes, purification methods and time intervals were identified. We provide 11 analytical performance metrics for exRNA quantification methods and put forward recommendations for both users and manufacturers of RNA purification methods and blood collection tubes, collectively, essential groundwork for exRNA-based precision medicine applications.

关键词：

TransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Zhiyue Zhao, Yao Xu, Yanxun Department of Applied Mathematics and Statistics Johns Hopkins University 3100 Wyman Park Dr BaltimoreMD21211 United States Division of Biostatistics and Bioinformatics School of Medicine Johns Hopkins University 733 N Broadway BaltimoreMD21205 United States

In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events, and survival data while accounting for their dependencies is critical. While joint models for the three components exist in statistical literature, many of these approaches are limited by heavy parametric assumptions and scalability issues. Recently, incorporating deep learning techniques into joint modeling has shown promising results. However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events. In this paper, we develop TransformerLSR, a flexible transformer-based deep modeling and inference framework to jointly model all three components simultaneously. TransformerLSR integrates deep temporal point processes into the joint modeling framework, treating recurrent and terminal events as two competing processes dependent on past longitudinal measurements and recurrent event times. Additionally, TransformerLSR introduces a novel trajectory representation and model architecture to potentially incorporate a priori knowledge of known latent structures among concurrent longitudinal variables. We demonstrate the effectiveness and necessity of TransformerLSR through simulation studies and analyzing a real-world medical dataset on patients after kidney transplantation. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

High Prevalence of Non-Hip Non-Spine Fractures Among Post Menopausal Women with Rheumatoid Arthritis

学校读者我要写书评

暂无评论

Journal of Clinical Densitometry 2010年第1期13卷 116-117页

作者： Rell-Bakalarska, Maria, MD, PhD Franek, Edward, Prof Rell, Krzysztof, MD PhD Gozdowskibachelor, Dariusz Rheuma Medicus Rheumatology and Osteoporosis Outpatient Clinic Warsaw Poland Department of Internal Diseases Endocrinology and Diabetology CSK MSWiA Warsaw Poland Emergency Ward Infant Jesus Clinical Hospital - Injuries Treatment Center Warsaw Poland Department of Applied Statistics and Bioinformatics Warsaw University of Life Science Warsaw Poland