Sparse Multivariate Reduced Rank Regression With Covariance Estimation
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Sparse Multivariate Reduced Rank Regression With Covariance Estimation
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Author : Khalif Aly Halani
language : en
Publisher:
Release Date : 2016
Sparse Multivariate Reduced Rank Regression With Covariance Estimation written by Khalif Aly Halani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Multivariate multiple linear regression is multiple linear regression, but with multiple responses. Standard approaches assume that observations from different subjects are uncorrelated and so estimates of the regression parameters can be obtained through separate univariate regressions, regardless of whether the responses are correlated within subjects. There are three main extensions to the simplest model. The first assumes a low rank structure on the coefficient matrix that arises from a latent factor model linking predictors to responses. The second reduces the number of parameters through variable selection. The third allows for correlations between response variables in the low rank model. Chen and Huang propose a new model that falls under the reduced-rank regression framework, employs variable selection, and estimates correlations among error terms. This project reviews their model, describes its implementation, and reports the results of a simulation study evaluating its performance. The project concludes with ideas for further research.
Multivariate Reduced Rank Regression
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Author : Gregory C. Reinsel
language : en
Publisher: Springer Nature
Release Date : 2022-11-30
Multivariate Reduced Rank Regression written by Gregory C. Reinsel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Mathematics categories.
This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.
High Dimensional Covariance Estimation
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Author : Mohsen Pourahmadi
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
High Dimensional Covariance Estimation written by Mohsen Pourahmadi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Mathematics categories.
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.
Statistical Applications From Clinical Trials And Personalized Medicine To Finance And Business Analytics
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Author : Jianchang Lin
language : en
Publisher: Springer
Release Date : 2016-11-13
Statistical Applications From Clinical Trials And Personalized Medicine To Finance And Business Analytics written by Jianchang Lin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-13 with Medical categories.
The papers in this volume represent a broad, applied swath of advanced contributions to the 2015 ICSA/Graybill Applied Statistics Symposium of the International Chinese Statistical Association, held at Colorado State University in Fort Collins. The contributions cover topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. Each papers was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe.
Information Processing And Management Of Uncertainty In Knowledge Based Systems
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Author : Marie-Jeanne Lesot
language : en
Publisher: Springer Nature
Release Date : 2020-06-05
Information Processing And Management Of Uncertainty In Knowledge Based Systems written by Marie-Jeanne Lesot and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-05 with Computers categories.
This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.
Learning And Intelligent Optimization
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Author : Ilias S. Kotsireas
language : en
Publisher: Springer Nature
Release Date : 2020-07-17
Learning And Intelligent Optimization written by Ilias S. Kotsireas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-17 with Mathematics categories.
This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting.
Progress In Pattern Recognition Image Analysis Computer Vision And Applications
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Author : Ruben Vera-Rodriguez
language : en
Publisher: Springer
Release Date : 2019-03-02
Progress In Pattern Recognition Image Analysis Computer Vision And Applications written by Ruben Vera-Rodriguez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-02 with Computers categories.
This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis
Handbook Of Robust Low Rank And Sparse Matrix Decomposition
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Author : Thierry Bouwmans
language : en
Publisher: CRC Press
Release Date : 2016-09-20
Handbook Of Robust Low Rank And Sparse Matrix Decomposition written by Thierry Bouwmans and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Computers categories.
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.
High Dimensional Statistics
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Author : Martin J. Wainwright
language : en
Publisher: Cambridge University Press
Release Date : 2019-02-21
High Dimensional Statistics written by Martin J. Wainwright and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Business & Economics categories.
A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.
Advanced Data Mining And Applications
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Author : Jinyan Li
language : en
Publisher: Springer
Release Date : 2016-11-17
Advanced Data Mining And Applications written by Jinyan Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-17 with Computers categories.
This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.