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Author : Shulian Shang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Download or read book Some Statistical Methods on Design, Modeling and Analysis of High-Dimensional Data written by Shulian Shang and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Chinghway Lim
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.:/5 (785 download)
Download or read book Modeling High Dimensional Data written by Chinghway Lim and published by . This book was released on 2011 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is on high dimensional data and their associated regularization through dimension reduction and penalization. We start with two real world problems to illustrate the practical difficulties and remedies in analyzing high dimensional data. In Chapter 1, we are tasked with modeling and predicting the U.S. stock market, where the number of stocks far exceeds the number of days relevant to the current market. Through an existing statistical arbitrage framework, we reduce the dimension of our problem with the use of correspondence analysis. We develop a data driven regression model and highlight some common statistical methods that improve our predictions. In Chapter 2, we attempt to detect and predict system anomalies in large enterprise telephony systems. We do this by processing large amounts of unstructured log files, again with dimension reduction methods, allowing effective visualization and automatic filtering of results. We then move on to more general methodology and analysis in high dimensions. In Chapter 3, we consider regularization methods, often used in dealing with high dimensional data, and tackle the problem of selecting the associated regularization parameter. We introduce SSCV, a selection criterion based on statistical stability, but also incorporating model fit, and show that it can often outperform the popular cross validation. Finally, we explore robust methods in the high dimensional setting in Chapter 4. We focus on the relative performance and distributional robustness of the estimators optimizing L1 and L2 loss functions respectively. We verify some expected results and also highlight cases where results from classical asymptotics fail, setting the stage for future theoretical work.
Author : Pietro Mantovan
Publisher : Springer Science & Business Media
ISBN 13 : 8847013860
Total Pages : 170 pages
Book Rating : 4.8/5 (47 download)
Download or read book Complex Data Modeling and Computationally Intensive Statistical Methods written by Pietro Mantovan and published by Springer Science & Business Media. This book was released on 2011-01-27 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.
Author : Christophe Giraud
Publisher : Chapman and Hall/CRC
ISBN 13 : 9781482237948
Total Pages : 0 pages
Book Rating : 4.2/5 (379 download)
Download or read book Introduction to High-Dimensional Statistics written by Christophe Giraud and published by Chapman and Hall/CRC. This book was released on 2014-12-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.
Author : Anna Maria Paganoni
Publisher : Springer
ISBN 13 : 3319111493
Total Pages : 210 pages
Book Rating : 4.3/5 (191 download)
Download or read book Advances in Complex Data Modeling and Computational Methods in Statistics written by Anna Maria Paganoni and published by Springer. This book was released on 2014-11-04 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Author : Inge Koch
Publisher : Cambridge University Press
ISBN 13 : 0521887933
Total Pages : 531 pages
Book Rating : 4.5/5 (218 download)
Download or read book Analysis of Multivariate and High-Dimensional Data written by Inge Koch and published by Cambridge University Press. This book was released on 2014 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.
Author : Tianwen Tony Cai
Publisher : World Scientific Publishing Company Incorporated
ISBN 13 : 9789814324854
Total Pages : 307 pages
Book Rating : 4.3/5 (248 download)
Download or read book High-dimensional Data Analysis written by Tianwen Tony Cai and published by World Scientific Publishing Company Incorporated. This book was released on 2011 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research. The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.
Author : Christophe Giraud
Publisher : CRC Press
ISBN 13 : 1000408353
Total Pages : 410 pages
Book Rating : 4.0/5 (4 download)
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.
Author : Arnoldo Frigessi
Publisher : Springer
ISBN 13 : 3319270990
Total Pages : 313 pages
Book Rating : 4.3/5 (192 download)
Download or read book Statistical Analysis for High-Dimensional Data written by Arnoldo Frigessi and published by Springer. This book was released on 2016-02-16 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.
Author : Ding-Geng Chen
Publisher : Springer
ISBN 13 : 9811025940
Total Pages : 229 pages
Book Rating : 4.8/5 (11 download)
Download or read book Advanced Statistical Methods in Data Science written by Ding-Geng Chen and published by Springer. This book was released on 2016-11-30 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.
Author : Pierre Duchesne
Publisher : Springer Science & Business Media
ISBN 13 : 9780387245546
Total Pages : 354 pages
Book Rating : 4.2/5 (455 download)
Download or read book Statistical Modeling and Analysis for Complex Data Problems written by Pierre Duchesne and published by Springer Science & Business Media. This book was released on 2005-04-12 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.
Author : Janine Bennett
Publisher : Springer
ISBN 13 : 3662449005
Total Pages : 297 pages
Book Rating : 4.6/5 (624 download)
Download or read book Topological and Statistical Methods for Complex Data written by Janine Bennett and published by Springer. This book was released on 2014-11-19 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
Author :
Publisher :
ISBN 13 : 9788847013926
Total Pages : 176 pages
Book Rating : 4.0/5 (139 download)
Download or read book Complex Data Modeling and Computationally Intensive Statistical Methods written by and published by . This book was released on 2011-08-14 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 942 pages
Book Rating : 4.4/5 (295 download)
Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author : Carlos Fernandez-Lozano
Publisher : MDPI
ISBN 13 : 3036509445
Total Pages : 314 pages
Book Rating : 4.0/5 (365 download)
Download or read book Statistical Inference from High Dimensional Data written by Carlos Fernandez-Lozano and published by MDPI. This book was released on 2021-04-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Real-world problems can be high-dimensional, complex, and noisy • More data does not imply more information • Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A process with multidimensional information is not necessarily easy to interpret nor process • In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth • The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data
Author : Lukas Meier
Publisher : CRC Press
ISBN 13 : 1000776778
Total Pages : 174 pages
Book Rating : 4.0/5 (7 download)
Download or read book ANOVA and Mixed Models written by Lukas Meier and published by CRC Press. This book was released on 2022-11-04 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R. From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R. The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications. Features Accessible to readers with a basic background in probability and statistics Covers fundamental concepts of experimental design and cause-effect relationships Introduces classical ANOVA models, including contrasts and multiple testing Provides an example-based introduction to mixed models Features basic concepts of split-plot and incomplete block designs R code available for all steps Supplementary website with additional resources and updates are available here. This book is primarily aimed at students, researchers, and practitioners from all areas who wish to analyze corresponding data with R. Readers will learn a broad array of models hand-in-hand with R, including the applications of some of the most important add-on packages.
Author : Zhaoxue Tong
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Download or read book Robust Techniques for High-dimensional Data written by Zhaoxue Tong and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation aims to develop statistical methods to address the challenges in modeling high-dimensional data caused by the presence of large amounts of noisy and ultra-high-dimensional data. The work focuses on fundamental theory and methodology in high-dimensional data analysis, including feature screening, false discovery rate control, robust regression, and precision matrix estimation. The first project introduces a new model-free conditional feature screening approach that is robust to outliers and heavy-tailed predictors and responses. Additionally, an FDR control procedure is proposed to enhance the performance of the screening procedure. We provide theoretical guarantees for the sure screening and false discovery control performance. We also present finite sample performance comparisons with existing methods through Monte Carlo simulation studies and a real data example. The second project proposes a new robust estimator that can handle both heavy-tailed predictors and heavy-tailed errors in high-dimensional regression. The estimator employs rank-based regression and winsorizes heavy-tailed predictors, with a focus on reducing the burden of tuning. The work establishes sufficient conditions for statistical consistency and demonstrates the strong oracle property through a second-stage enhancement. Both simulation studies and real data analysis demonstrate good performance. The third project presents a new approach for estimating the precision matrix for high-dimensional heavy-tailed data. The proposed estimator employs winsorized rank-based regression and eliminates the burden of fine-tuning, providing robustness guarantees and computational efficiency. We establish sufficient conditions for statistical consistency and propose a robust variance estimator for heavy-tailed data based on the median-of-means approach, which performs well in simulation studies.
