Автор: Bickel, David Название: Genomics data analysis ISBN: 0367280361 ISBN-13(EAN): 9780367280369 Издательство: Taylor&Francis Рейтинг: Цена: 58170.00 T Наличие на складе: Невозможна поставка. Описание: The objective is to prepare students and scientists to analyze genomics data using empirical Bayes methods and to critically evaluate the statistical methods appearing in genomics articles. That is accomplished by providing the information needed for them to interpret p values for their current or future research.
Автор: V.I. Serdobolskii Название: Multivariate Statistical Analysis ISBN: 0792366433 ISBN-13(EAN): 9780792366430 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presents a branch of mathematical statistics which intends to construct unimprovable methods of multivariate analysis, multi-parametric estimation, and discriminant and regression analysis. This work is suitable for researchers and graduate students whose work involves statistics and probability, reliability and risk analysis, and econometrics.
Автор: V.I. Serdobolskii Название: Multivariate Statistical Analysis ISBN: 9048155932 ISBN-13(EAN): 9789048155934 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Multivariate Statistical Analysis
Автор: Kawaguchi, Atsushi Название: Multivariate Analysis for Neuroimaging Data ISBN: 0367255324 ISBN-13(EAN): 9780367255329 Издательство: Taylor&Francis Рейтинг: Цена: 158230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book enables us to analyze statistically brain imaging data. It is meant for a wide range of researchers interested in biostatistics, data science, and neuroscience. It is useful to understand the background theory of standard software for neuroimaging data analysis.
Автор: J.D. Jobson Название: Applied Multivariate Data Analysis ISBN: 1461269474 ISBN-13(EAN): 9781461269472 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software.
Автор: Bartholomew Название: Analysis of Multivariate Social Science Data ISBN: 1138464546 ISBN-13(EAN): 9781138464544 Издательство: Taylor&Francis Рейтинг: Цена: 199050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Exploring how to use key multivariate methods in the social sciences, this book contains three chapters on regression analysis, confirmatory factor analysis and structural equation models, and multilevel models. It presents various examples of real-world applications and establishes an approach to latent variable modeling.
Автор: Warton, David I Название: Eco-stats - data analysis in ecology ISBN: 3030884422 ISBN-13(EAN): 9783030884420 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way.
Автор: Kimmo Vehkalahti , Brian S. Everitt Название: Multivariate Analysis for the Behavioral Sciences, Second Edition ISBN: 0815385153 ISBN-13(EAN): 9780815385158 Издательство: Taylor&Francis Рейтинг: Цена: 86760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists.
Автор: Bickel, David R. Название: Genomics Data Analysis ISBN: 1032475285 ISBN-13(EAN): 9781032475288 Издательство: Taylor&Francis Рейтинг: Цена: 22450.00 T Наличие на складе: Нет в наличии.
Автор: Shuichi Shinmura Название: High-dimensional Microarray Data Analysis ISBN: 9811359970 ISBN-13(EAN): 9789811359972 Издательство: Springer Рейтинг: Цена: 93160.00 T 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: 139750.00 T 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.
Автор: Pourahmadi Mohsen Название: High-dimensional Covariance Estimation ISBN: 1118034295 ISBN-13(EAN): 9781118034293 Издательство: Wiley Рейтинг: Цена: 84430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
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