Applied Biclustering Methods for Big and High-Dimensional Data Using R, Kasim Adetayo, Shkedy Ziv, Kaiser Sebastian
Автор: Koch Название: Analysis of Multivariate and High-Dimensional Data ISBN: 0521887933 ISBN-13(EAN): 9780521887939 Издательство: Cambridge Academ Рейтинг: Цена: 10613.00 р. Наличие на складе: Поставка под заказ.
Описание: `Big data` poses challenges that require both classical multivariate methods and modern machine-learning techniques. This coherent treatment integrates theory with data analysis, visualisation and interpretation of the analysis. Problems, data sets and MATLAB (R) code complete the package. It is suitable for master`s/graduate students in statistics and working scientists in data-rich disciplines.
Автор: Arnoldo Frigessi; Peter B?hlmann; Ingrid Glad; Met Название: Statistical Analysis for High-Dimensional Data ISBN: 3319270974 ISBN-13(EAN): 9783319270975 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyv gar, 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.
Автор: Shuichi Shinmura Название: High-dimensional Microarray Data Analysis ISBN: 9811359970 ISBN-13(EAN): 9789811359972 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks.Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel.Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.
Автор: Frigessi Arnoldo, Buhlmann Peter, Glad Ingrid Название: Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014 ISBN: 3319800736 ISBN-13(EAN): 9783319800738 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Some Themes in High-Dimensional Statistics: A. Frigessi et al.- Laplace Appoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton et al.- Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration: L.C. Bergersen, I. Glad et al.- Spectral Clustering and Block Models: a Review and a new Algorithm: S. Bhattacharyya et al.- Bayesian Hierarchical Mixture Models: L. Bottelo et al.- iBATCGH; Integrative Bayesian Analysis of Transcriptomic and CGH Data: Cassese, M. Vannucci et al.- Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West.- Combining Single and Paired End RNA-seq Data for Differential Expression Analysis: F. Feng, T.Speed et al.- An Imputation Method for Estimation the Learning Curve in Classification Problems: E. Laber et al.- Baysian Feature Allocation Models for Tumor Heterogeneity: J. Lee, P. Mueller et al.- Bayesian Penalty Mixing: The Case of a Non-Separable Penalty: V. Rockova et al.- Confidence Intervals for Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al.- Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al.
Описание: This monograph is devoted to random walk based stochastic algorithms for solving high-dimensional boundary value problems of mathematical physics and chemistry. It includes Monte Carlo methods where the random walks live not only on the boundary, but also inside the domain. A variety of examples from capacitance calculations to electron dynamics in semiconductors are discussed to illustrate the viability of the approach.The book is written for mathematicians who work in the field of partial differential and integral equations, physicists and engineers dealing with computational methods and applied probability, for students and postgraduates studying mathematical physics and numerical mathematics. Contents: IntroductionRandom walk algorithms for solving integral equationsRandom walk-on-boundary algorithms for the Laplace equationWalk-on-boundary algorithms for the heat equationSpatial problems of elasticityVariants of the random walk on boundary for solving stationary potential problemsSplitting and survival probabilities in random walk methods and applicationsA random WOS-based KMC method for electron-hole recombinationsMonte Carlo methods for computing macromolecules properties and solving related problemsBibliography
Автор: Bai Zhidong Название: Spectral Theory of Large Dimensional Random Matrices and its ISBN: 981457905X ISBN-13(EAN): 9789814579056 Издательство: World Scientific Publishing Цена: 12830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book contains three parts: Spectral theory of large dimensional random matrices; Applications to wireless communications; and Applications to finance. In the first part, we introduce some basic theorems of spectral analysis of large dimensional random matrices that are obtained under finite moment conditions, such as the limiting spectral distributions of Wigner matrix and that of large dimensional sample covariance matrix, limits of extreme eigenvalues, and the central limit theorems for linear spectral statistics. In the second part, we introduce some basic examples of applications of random matrix theory to wireless communications and in the third part, we present some examples of Applications to statistical finance.
Автор: Peter B?hlmann; Sara van de Geer Название: Statistics for High-Dimensional Data ISBN: 3642268579 ISBN-13(EAN): 9783642268571 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Название: High-dimensional data analysis ISBN: 981432485X ISBN-13(EAN): 9789814324854 Издательство: World Scientific Publishing Рейтинг: Цена: 15048.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Over the 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. This book intends to examine the issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction.
Автор: B?hlmann Название: Statistics for High Dimensional Data ISBN: 3642201911 ISBN-13(EAN): 9783642201912 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Автор: Ghysels, Eric (edward M. Bernstein Distinguished Professor Of Economics And Professor Of Finance, Kenan-flagler School Of Business, University Of Nort Название: Applied economic forecasting using time series methods ISBN: 0190622016 ISBN-13(EAN): 9780190622015 Издательство: Oxford Academ Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.
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