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Simultaneous Inference For High Dimensional And Correlated Data
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Book Synopsis Simultaneous Inference for High Dimensional and Correlated Data by : Afroza Polin
Download or read book Simultaneous Inference for High Dimensional and Correlated Data written by Afroza Polin and published by . This book was released on 2019 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: In high dimensional data, the number of covariates is larger than the sample size, which makes the estimation process challenging. We consider a high-dimensional and longitudinal data where at each time point, the number of covariates is much higher than the number of subjects. We consider two different settings of longitudinal data. First, we consider that the samples at different time points are generated from different populations. Second, we consider that the samples at different time points are generated from a multivariate distribution. In both cases, the number of covariates is much larger than the sample size and the standard least square methods are not applicable.In longitudinal study, our main focus is in the changes of the mean responses over the time and how these changes are related to the explanatory variables. Thus we are interested in testing the effect of the covariates over the time points simultaneously. In the first scenario, we use lasso at each time point to regress the response on the explanatory variables. Along with estimating the regression coefficients lasso also does dimension reduction. We use de-biased lasso for inference. To adjust the multiplicity effect in simultaneous testing we apply Bonferroni, Holm's, Hochberg's and the coherent stepwise procedures. In the second scenario, the samples at different time points are generated from a multivariate distribution and the dimension of the multivariate distribution is equal to the number of time points. We use lasso and de-biased lasso for inferences. To adjust the multiplicity effect in simultaneous testing, we use Bonferroni, Holm's, Hochberg's and stepwise procedures. We provide theoretical details that Bonferroni, Holm's step-down and the coherent step-wise procedures controls the family-wise error rate in strong sense for de-biased lasso estimators. While Hochberg's procedure provides a strong control of family-wise error rate only for independent or positively correlated test statistics.
Book Synopsis Valid Simultaneous Inference in High-dimensional Settings (with the HDM Package for R) by : Philipp Bach
Download or read book Valid Simultaneous Inference in High-dimensional Settings (with the HDM Package for R) written by Philipp Bach and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and efficient hypothesis tests for a potentially large number of coefficients as well as the construction of simultaneous confidence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO.
Book Synopsis Simultaneous Statistical Inference by : Thorsten Dickhaus
Download or read book Simultaneous Statistical Inference written by Thorsten Dickhaus and published by Springer Science & Business Media. This book was released on 2014-01-23 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.
Book Synopsis Partially Linear Models by : Wolfgang Härdle
Download or read book Partially Linear Models written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.
Book Synopsis Simultaneous Inference on Sample Covariances by : Han Xiao
Download or read book Simultaneous Inference on Sample Covariances written by Han Xiao and published by . This book was released on 2011 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis considers the maximum deviations of the sample covariances in the contexts of high dimensional data analysis and time series analysis.
Book Synopsis Epigenetic Biomarker and Personalized Precision Medicine by : Jiucun Wang
Download or read book Epigenetic Biomarker and Personalized Precision Medicine written by Jiucun Wang and published by Frontiers Media SA. This book was released on 2020-12-21 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Book Synopsis Resampling-Based Multiple Testing by : Peter H. Westfall
Download or read book Resampling-Based Multiple Testing written by Peter H. Westfall and published by John Wiley & Sons. This book was released on 1993-01-12 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.
Book Synopsis High-Dimensional Probability by : Roman Vershynin
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Book Synopsis Large-Scale Global and Simultaneous Inference by : Tony Cai
Download or read book Large-Scale Global and Simultaneous Inference written by Tony Cai and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to rapid technological advances, researchers are now able to collect and analyze ever larger data sets. Statistical inference for big data often requires solving thousands or even millions of parallel inference problems simultaneously. This poses significant challenges and calls for new principles, theories, and methodologies. This review provides a selective survey of some recently developed methods and results for large-scale statistical inference, including detection, estimation, and multiple testing. We begin with the global testing problem, where the goal is to detect the existence of sparse signals in a data set, and then move to the problem of estimating the proportion of nonnull effects. Finally, we focus on multiple testing with false discovery rate (FDR) control. The FDR provides a powerful and practical approach to large-scale multiple testing and has been successfully used in a wide range of applications. We discuss several effective data-driven procedures and also present efficient strategies to handle various grouping, hierarchical, and dependency structures in the data.
Book Synopsis Advances and Innovations in Statistics and Data Science by : Wenqing He
Download or read book Advances and Innovations in Statistics and Data Science written by Wenqing He and published by Springer Nature. This book was released on 2022-10-27 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.
Book Synopsis Selection and Estimation for Large-scale Simultaneous Inference by : Bradley Efron
Download or read book Selection and Estimation for Large-scale Simultaneous Inference written by Bradley Efron and published by . This book was released on 2004 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Big Data Analytics by : Wolfgang Karl Härdle
Download or read book Handbook of Big Data Analytics written by Wolfgang Karl Härdle and published by Springer. This book was released on 2018-07-20 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
Book Synopsis Introduction to High-Dimensional Statistics by : Christophe Giraud
Download or read book Introduction to High-Dimensional Statistics written by Christophe Giraud and published by CRC Press. This book was released on 2021-08-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research." —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples.
Book Synopsis Handbook of Bayesian, Fiducial, and Frequentist Inference by : James Berger
Download or read book Handbook of Bayesian, Fiducial, and Frequentist Inference written by James Berger and published by CRC Press. This book was released on 2024-02-26 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference. Key Features: Provides a comprehensive introduction to the key developments in the BFF schools of inference Gives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledge Is accessible for readers with different perspectives and backgrounds
Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed
Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Book Synopsis Dimension Reduction and High-dimensional Data by : Maxime Turgeon
Download or read book Dimension Reduction and High-dimensional Data written by Maxime Turgeon and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Recent technological advances in many domains including both genomics and brain imaging have led to an abundance of high-dimensional and correlated data being routinely collected. A widespread analytical goal in these fields is to investigate the relationships between, on the one hand, a group of genomic markers or anatomical brain measurements and, on theother hand, a set of clinical variables or phenotypes. To leverage the correlation within each set of measurements, and to improve the interpretability of a measure of the association, one can use dimension reduction techniques: one, or both, group of variables can be summarised by a small set of latent features that summarise the structure of interest andcapture association through an appropriately chosen statistic. But the high-dimensionality of contemporary datasets brings many computational and theoretical challenges, and most classical multivariate methods cannot be used directly.This thesis is comprised primarily of three manuscripts that investigate the issues related to measuring association in high dimensional datasets. In the first manuscript, I explore the optimality properties of a dimension reduction method known as Principal Component of Explained Variance (PCEV). This method seeks a linear combination of the outcome variablesthat maximises the proportion of variance explained by a set of covariates of interest. I then explain how PCEV can be extended to a computationally simple and efficient estimation strategy for high-dimensional outcomes (p > n) that relies on a "block-independence" assumption. In the second manuscript, I study the problem of inference with high-dimensional datasets: given two datasets Y and X, with one or both being high-dimensional, how can we perform a test of association in a computationally efficient way? Specifically, I look at the set of multivariate methods that can be described as a double Wishart problem; PCEV, Canonical Correlation Analysis (CCA), and Multivariate Analysis of Variance (MANOVA) are all examples of double Wishart problems. I show that valid high-dimensional p-values can be derived using an empirical estimator of the null distribution. This is achieved by performing a small number of permutations, and then fitting a location-scale family of the Tracy-Widom distribution of order 1 to the test statistics computed from the permuted data. Finally, in the third manuscript, I apply the concepts developed in the two other manuscripts to a data analysis of targeted custom capture bisulfite methylation data. I show how PCEV can be used in conjunction with the ideas in the second manuscript to test for a region-level association between the methylation levels of CpG dinucleotides and levels of anti-citrullinated protein antibody (ACPA), an antigen thought to be a predictor of rheumatoid arthritis onset. In this study, the CpG dinucleotides are naturally grouped by design, and several of these groups contain a number of methylation measurements that is larger than the samplesize." --
Book Synopsis Multivariate T-Distributions and Their Applications by : Samuel Kotz
Download or read book Multivariate T-Distributions and Their Applications written by Samuel Kotz and published by Cambridge University Press. This book was released on 2004-02-16 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Almost all the results available in the literature on multivariate t-distributions published in the last 50 years are now collected together in this comprehensive reference. Because these distributions are becoming more prominent in many applications, this book is a must for any serious researcher or consultant working in multivariate analysis and statistical distributions. Much of this material has never before appeared in book form. The first part of the book emphasizes theoretical results of a probabilistic nature. In the second part of the book, these are supplemented by a variety of statistical aspects. Various generalizations and applications are dealt with in the final chapters. The material on estimation and regression models is of special value for practitioners in statistics and economics. A comprehensive bibliography of over 350 references is included.
