Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Christian Robert; George Casella Название: Monte Carlo Statistical Methods ISBN: 1441919392 ISBN-13(EAN): 9781441919397 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Автор: Chandrupatla, Tirupathi, Belegundu, Ashok Название: Introduction to finite elements in engineering ISBN: 1108841414 ISBN-13(EAN): 9781108841412 Издательство: Cambridge Academ Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Thoroughly updated with improved pedagogy, the fifth edition provides senior undergraduate and graduate students with a clear, comprehensive introduction to the field. Features enhanced coverage of introductory topics, over thirty additional solved problems; downloadable Matlab, Python, C, and Javascript code; and solutions for instructors.
Автор: Mehmetoglu, Mehmet Mittner, Matthias Название: Applied statistics using r ISBN: 1526476223 ISBN-13(EAN): 9781526476227 Издательство: Sage Publications Рейтинг: Цена: 8235.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Drawing on real world data to showcase different techniques, this practical book helps you use R for data analysis in your own research.
Название: Understanding Statistical Concepts Using S-plus ISBN: 0805836233 ISBN-13(EAN): 9780805836233 Издательство: Taylor&Francis Рейтинг: Цена: 7654.00 р. Наличие на складе: Нет в наличии.
Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data.
The book's four parts teach:
Core knowledge of R and programming concepts
How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation
Essentials of probability, random variables, and expectation required to understand simulation
Stochastic modelling and simulation, including random number generation and Monte Carlo integration
In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size.
Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables.
Building readers' statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.
Описание: Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other Earth science subjects, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB.
Автор: Deshmukh Shailaja, Kulkarni Madhuri Название: Asymptotic Statistical Inference: A Basic Course Using R ISBN: 9811590028 ISBN-13(EAN): 9789811590023 Издательство: Springer Рейтинг: Цена: 9756.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The book also discusses a score test and Wald`s test, their relationship with the likelihood ratio test and Karl Pearson`s chi-square test.
Автор: West, Brady T. (university Of Michigan, Ann Arbor, Usa) Welch, Kathleen B. (university Of Michigan, Ann Arbor, Usa) Galecki, Andrzej T (university Of Название: Linear mixed models ISBN: 1032019328 ISBN-13(EAN): 9781032019321 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. There is a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included
Автор: Tadesse Mahlet G., Vannucci Marina Название: Handbook of Bayesian Variable Selection ISBN: 0367543761 ISBN-13(EAN): 9780367543761 Издательство: Taylor&Francis Рейтинг: Цена: 26030.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions.
Автор: Lu Название: Handbook of Statistical Bioinformatics ISBN: 3662659018 ISBN-13(EAN): 9783662659014 Издательство: Springer Рейтинг: Цена: 24392.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.
Автор: Pardo Название: Statistical Methods and Analyses for Medical Devices ISBN: 3031261380 ISBN-13(EAN): 9783031261381 Издательство: Springer Рейтинг: Цена: 24392.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a reference for people working in the design, development, and manufacturing of medical devices. While there are no statistical methods specifically intended for medical devices, there are methods that are commonly applied to various problems in the design, manufacturing, and quality control of medical devices. The aim of this book is not to turn everyone working in the medical device industries into mathematical statisticians; rather, the goal is to provide some help in thinking statistically, and knowing where to go to answer some fundamental questions, such as justifying a method used to qualify/validate equipment, or what information is necessary to support the choice of sample sizes. While, there are no statistical methods specifically designed for analysis of medical device data, there are some methods that seem to appear regularly in relation to medical devices. For example, the assessment of receiver operating characteristic curves is fundamental to development of diagnostic tests, and accelerated life testing is often critical for assessing the shelf life of medical device products. Another example is sensitivity/specificity computations are necessary for in-vitro diagnostics, and Taguchi methods can be very useful for designing devices. Even notions of equivalence and noninferiority have different interpretations in the medical device field compared to pharmacokinetics. It contains topics such as dynamic modeling, machine learning methods, equivalence testing, and experimental design, for example. This book is for those with no statistical experience, as well as those with statistical knowledgeable—with the hope to provide some insight into what methods are likely to help provide rationale for choices relating to data gathering and analysis activities for medical devices.
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