Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling, Thulin, M?ns
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
Автор: Do Название: Advances in Statistical Bioinformatics ISBN: 1107027527 ISBN-13(EAN): 9781107027527 Издательство: Cambridge Academ Рейтинг: Цена: 22176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This material is written for statisticians who are interested in modeling and analyzing high-throughput data.
Автор: Geisser, Seymour Название: Predictive Inference ISBN: 0412034719 ISBN-13(EAN): 9780412034718 Издательство: Taylor&Francis Рейтинг: Цена: 27562.00 р. Наличие на складе: Нет в наличии.
Автор: Pavel V. Shevchenko Название: Modelling Operational Risk Using Bayesian Inference ISBN: 3642423531 ISBN-13(EAN): 9783642423536 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses.
Автор: Ratner Название: Statistical & Machine-Learning Data ISBN: 1498797601 ISBN-13(EAN): 9781498797603 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
Описание: This book provides a different lens through which students can view what happens in twenty-first-century schools while also considering the perspectives of multiple constituencies: parents, teachers, students and communities. Included is a wide range of scholarship in the foundations of education; essays range from the more traditional work of John Dewey to the controversial ideas of Henry Giroux.
Автор: Taylor Arnold; Lauren Tilton Название: Humanities Data in R ISBN: 3319366718 ISBN-13(EAN): 9783319366715 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility).
Автор: Il Do Ha; Jong-Hyeon Jeong; Youngjo Lee Название: Statistical Modelling of Survival Data with Random Effects ISBN: 9811349010 ISBN-13(EAN): 9789811349010 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Нет в наличии.
Описание: This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions.
Автор: Bradley Boehmke Название: Data Wrangling with R ISBN: 3319455982 ISBN-13(EAN): 9783319455983 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and datesThe difference between different data structures and how to create, add additional components to, and subset each data structureHow to acquire and parse data from locations previously inaccessibleHow to develop functions and use loop control structures to reduce code redundancyHow to use pipe operators to simplify code and make it more readableHow to reshape the layout of data and manipulate, summarize, and join data sets
