Large Scale Multiple Testing For High Dimensional Nonparanormal Data

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Large Scale Multiple Testing for High-Dimensional Nonparanormal Data

Author : Yanhui Xu
Publisher :
ISBN 13 :
Total Pages : 107 pages
Book Rating : 4.:/5 (128 download)

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Book Synopsis Large Scale Multiple Testing for High-Dimensional Nonparanormal Data by : Yanhui Xu

Download or read book Large Scale Multiple Testing for High-Dimensional Nonparanormal Data written by Yanhui Xu and published by . This book was released on 2019 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: False discovery control in high dimensional multiple testing has been frequently encountered in many scientific research. Under the multivariate normal distribution assumption, \cite{fan2012} proposed an approximate expression for false discovery proportion (FDP) in large-scale multiple testing when a common threshold is used and provided a consistent estimate of realized FDP when the covariance matrix is known. They further extended their study when the covariance matrix is unknown \citep{fan2017}. However, in reality, the multivariate normal assumption is often violated. In this paper, we relaxed the normal assumption by developing a testing procedure on nonparanormal distribution which extends the Gaussian family to a much larger population. The nonparanormal distribution is indeed a high dimensional Gaussian copula with nonparametric marginals. Estimating the underlying monotone functions is key to good FDP approximation. Our procedure achieved minimal mean error in approximating the FDP compared with other methods in simulation studies. We gave theoretical investigations regarding the performance of estimated covariance matrix and false rejections. In real dataset setting, our method was able to detect more differentiated genes while still maintaining the FDP under a small level. This thesis provides an important tool for approximating FDP in a given experiment where the normal assumption may not hold. We also developed a dependence-adjusted procedure which provides more power than fixed-threshold method. Our procedure also show robustness for heavy-tailed data under a variety of distributions in numeric studies.

Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures

Author : Tony Cai
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures by : Tony Cai

Download or read book Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures written by Tony Cai and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by a wide range of contemporary applications, statistical inference for covariance structures has been an active area of current research in high-dimensional statistics. This review provides a selective survey of some recent developments in hypothesis testing for high-dimensional covariance structures, including global testing for the overall pattern of the covariance structures and simultaneous testing of a large collection of hypotheses on the local covariance structures with false discovery proportion and false discovery rate control. Both one-sample and two-sample settings are considered. The specific testing problems discussed include global testing for the covariance, correlation, and precision matrices, and multiple testing for the correlations, Gaussian graphical models, and differential networks.

Statistical Methods for the Analysis of Genomic Data

Author : Hui Jiang
Publisher : MDPI
ISBN 13 : 3039361406
Total Pages : 136 pages
Book Rating : 4.0/5 (393 download)

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Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang

Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by MDPI. This book was released on 2020-12-29 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Large Scale Multiple Testing for Data with Spatial Signals

Author : Yunda Zhong
Publisher :
ISBN 13 : 9781303005732
Total Pages : 107 pages
Book Rating : 4.0/5 (57 download)

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Book Synopsis Large Scale Multiple Testing for Data with Spatial Signals by : Yunda Zhong

Download or read book Large Scale Multiple Testing for Data with Spatial Signals written by Yunda Zhong and published by . This book was released on 2013 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis consists of three projects. The abstracts for each project are listed below.

Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Author : Christine Sinoquet
Publisher : Oxford University Press, USA
ISBN 13 : 0198709021
Total Pages : 483 pages
Book Rating : 4.1/5 (987 download)

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Book Synopsis Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by : Christine Sinoquet

Download or read book Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics written by Christine Sinoquet and published by Oxford University Press, USA. This book was released on 2014 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play.

Multiple Testing Under Dependence with Approximate Posterior Likelihood and Related Topics

Author : Sairam D. Rayaprolu
Publisher :
ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.:/5 (896 download)

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Book Synopsis Multiple Testing Under Dependence with Approximate Posterior Likelihood and Related Topics by : Sairam D. Rayaprolu

Download or read book Multiple Testing Under Dependence with Approximate Posterior Likelihood and Related Topics written by Sairam D. Rayaprolu and published by . This book was released on 2013 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Some New Developments on Multiple Testing Procedures

Author : Lilun Du
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis Some New Developments on Multiple Testing Procedures by : Lilun Du

Download or read book Some New Developments on Multiple Testing Procedures written by Lilun Du and published by . This book was released on 2015 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In chapter 2, we present a single-index modulated multiple testing procedure, which maintains control of the false discovery rate while incorporating prior information, by assuming the availability of a bivariate p-value for each hypothesis. To find the optimal rejection region for the bivariate p-value, we propose a criteria based on the ratio of probability density functions of the bivariate p-value under the true null and non-null. This criteria in the bivariate normal setting further motivates us to project the bivariate p-value to a single index p-value, for a wide range of directions. The true null distribution of the single index p-value is estimated via parametric and nonparametric approaches, leading to two procedures for estimating and controlling the false discovery rate. To derive the optimal projection direction, we propose a new approach based on power comparison, which is further shown to be consistent under some mild conditions. Multiple testing based on chi-squared test statistics is commonly used in many scientific fields such as genomics research and brain imaging studies. However, the challenges associated with designing a formal testing procedure when there exists a general dependence structure across the chi-squared test statistics have not been well addressed. In chapter 3, we propose a Factor Connected procedure to fill in this gap. We first adopt a latent factor structure to construct a testing framework for approximating the false discovery proportion (FDP) for a large number of highly correlated chi-squared test statistics with finite degrees of freedom k. The testing framework is then connected to simultaneously testing k linear constraints in a large dimensional linear factor model involved with some observable and unobservable common factors, resulting in a consistent estimator of FDP based on the associated unadjusted p-values.

A New Approach for Large Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses

Author : Wenge Guo
Publisher :
ISBN 13 :
Total Pages : 37 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis A New Approach for Large Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses by : Wenge Guo

Download or read book A New Approach for Large Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses written by Wenge Guo and published by . This book was released on 2018 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt:

High-Dimensional Covariance Estimation

Author : Mohsen Pourahmadi
Publisher : John Wiley & Sons
ISBN 13 : 1118034295
Total Pages : 204 pages
Book Rating : 4.1/5 (18 download)

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Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi

Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Effect of Cross-validation on the Output of Multiple Testing Procedures

Author : Josh Dallas Price
Publisher :
ISBN 13 :
Total Pages : 90 pages
Book Rating : 4.:/5 (115 download)

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Book Synopsis Effect of Cross-validation on the Output of Multiple Testing Procedures by : Josh Dallas Price

Download or read book Effect of Cross-validation on the Output of Multiple Testing Procedures written by Josh Dallas Price and published by . This book was released on 2019 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: High dimensional data with sparsity is routinely observed in many scientific disciplines. Filtering out the signals embedded in noise is a canonical problem in such situations requiring multiple testing. The Benjamini--Hochberg procedure using False Discovery Rate control is the gold standard in large scale multiple testing. In Majumder et al. (2009) an internally cross-validated form of the procedure is used to avoid a costly replicate study and the complications that arise from population selection in such studies (i.e. extraneous variables). I implement this procedure and run extensive simulation studies under increasing levels of dependence among parameters and different data generating distributions and compare results with other common techniques. I illustrate that the internally cross-validated Benjamini--Hochberg procedure results in a significantly reduced false discovery rate, while maintaining a reasonable, though increased, false negative rate, and in a reduction to inherent variability under strong dependence structures when compared with the usual Benjamini--Hochberg procedure. In the discussion section, I describe some possibilities for relevant applications and future studies.

Statistical Methods for Large-scale Multiple Testing Problems

Author : Yu Gao
Publisher :
ISBN 13 :
Total Pages : 100 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Statistical Methods for Large-scale Multiple Testing Problems by : Yu Gao

Download or read book Statistical Methods for Large-scale Multiple Testing Problems written by Yu Gao and published by . This book was released on 2019 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large-scale multiple testing problem simultaneously tests thousands or even millions of null hypotheses, and it is widely used in different fields, for example genetics and astronomy. An error rate serves as a measure of the performance of a testing procedure. The use of the family-wise error rate can accommodate any dependence between hypotheses, but it is often overly conservative and has limited detection power.The false discovery rate is more powerful, however not as widely used due to the requirement of independence and other reasons. In this thesis, we develop statistical methods for large-scale multiple testing problems in pharmacovigilance and genetic studies, and adopt the false discovery rate to improve the detection power by tacking mixed challenges.

High-Dimensional Data Analysis with Low-Dimensional Models

Author : John Wright
Publisher : Cambridge University Press
ISBN 13 : 1108805558
Total Pages : 718 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis High-Dimensional Data Analysis with Low-Dimensional Models by : John Wright

Download or read book High-Dimensional Data Analysis with Low-Dimensional Models written by John Wright and published by Cambridge University Press. This book was released on 2022-01-13 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

Signal Processing and Machine Learning for Biomedical Big Data

Author : Ervin Sejdic
Publisher : CRC Press
ISBN 13 : 149877346X
Total Pages : 624 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Signal Processing and Machine Learning for Biomedical Big Data by : Ervin Sejdic

Download or read book Signal Processing and Machine Learning for Biomedical Big Data written by Ervin Sejdic and published by CRC Press. This book was released on 2018-07-04 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Network Psychometrics with R

Author : Adela-Maria Isvoranu
Publisher : Taylor & Francis
ISBN 13 : 100054107X
Total Pages : 261 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Network Psychometrics with R by : Adela-Maria Isvoranu

Download or read book Network Psychometrics with R written by Adela-Maria Isvoranu and published by Taylor & Francis. This book was released on 2022-04-28 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader. Network Psychometrics with R is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods. This book is accompanied by a companion website with resources for both students and lecturers.

Analysis of Biological Networks

Author : Björn H. Junker
Publisher : John Wiley & Sons
ISBN 13 : 1118209915
Total Pages : 278 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis Analysis of Biological Networks by : Björn H. Junker

Download or read book Analysis of Biological Networks written by Björn H. Junker and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.

Optimization for Machine Learning

Author : Suvrit Sra
Publisher : MIT Press
ISBN 13 : 026201646X
Total Pages : 509 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Optimization for Machine Learning by : Suvrit Sra

Download or read book Optimization for Machine Learning written by Suvrit Sra and published by MIT Press. This book was released on 2012 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

The Wiley Handbook of Psychometric Testing

Author : Paul Irwing
Publisher : John Wiley & Sons
ISBN 13 : 1118489705
Total Pages : 1064 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis The Wiley Handbook of Psychometric Testing by : Paul Irwing

Download or read book The Wiley Handbook of Psychometric Testing written by Paul Irwing and published by John Wiley & Sons. This book was released on 2018-03-14 with total page 1064 pages. Available in PDF, EPUB and Kindle. Book excerpt: A must-have resource for researchers, practitioners, and advanced students interested or involved in psychometric testing Over the past hundred years, psychometric testing has proved to be a valuable tool for measuring personality, mental ability, attitudes, and much more. The word ‘psychometrics’ can be translated as ‘mental measurement’; however, the implication that psychometrics as a field is confined to psychology is highly misleading. Scientists and practitioners from virtually every conceivable discipline now use and analyze data collected from questionnaires, scales, and tests developed from psychometric principles, and the field is vibrant with new and useful methods and approaches. This handbook brings together contributions from leading psychometricians in a diverse array of fields around the globe. Each provides accessible and practical information about their specialist area in a three-step format covering historical and standard approaches, innovative issues and techniques, and practical guidance on how to apply the methods discussed. Throughout, real-world examples help to illustrate and clarify key aspects of the topics covered. The aim is to fill a gap for information about psychometric testing that is neither too basic nor too technical and specialized, and will enable researchers, practitioners, and graduate students to expand their knowledge and skills in the area. Provides comprehensive coverage of the field of psychometric testing, from designing a test through writing items to constructing and evaluating scales Takes a practical approach, addressing real issues faced by practitioners and researchers Provides basic and accessible mathematical and statistical foundations of all psychometric techniques discussed Provides example software code to help readers implement the analyses discussed