Statistical Methods for Handling Incomplete Data, Kim Jae Kwang, Shao Jun
Старое издание
Автор: Bo Shen; Zidong Wang; Huisheng Shu Название: Nonlinear Stochastic Systems with Incomplete Information ISBN: 1447160002 ISBN-13(EAN): 9781447160007 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Nonlinear Stochastic Processes shows the reader how to deal with the issue of network-induced incomplete information. It presents a unified framework for filtering and control problems in complex communication networks with limited bandwidth.
Автор: Norou Diawara Название: Modern Statistical Methods for Spatial and Multivariate Data ISBN: 3030114309 ISBN-13(EAN): 9783030114305 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This contributed volume features invited papers on current models and statistical methods for spatial and multivariate data. With a focus on recent advances in statistics, topics include spatio-temporal aspects, classification techniques, the multivariate outcomes with zero and doubly-inflated data, discrete choice modelling, copula distributions, and feasible algorithmic solutions. Special emphasis is placed on applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the Census dataset for the state of Iowa. Articles use simulated and aggregated data examples to show the flexibility and wide applications of proposed techniques.
Carefully peer-reviewed and pedagogically presented for a broad readership, this volume is suitable for graduate and postdoctoral students interested in interdisciplinary research. Researchers in applied statistics and sciences will find this book an important resource on the latest developments in the field. In keeping with the STEAM-H series, the editors hope to inspire interdisciplinary understanding and collaboration.
Описание: Investigates optimal investment problems for stochastic financial market models. This work is intended for academics and students who are interested in the mathematics of finance, stochastic processes, and optimal control, and also for practitioners in risk management and quantitative analysis.
Автор: Little Название: Statistical Analysis with Missing Data, Third Edit ion ISBN: 0470526793 ISBN-13(EAN): 9780470526798 Издательство: Wiley Рейтинг: Цена: 12664.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.
Автор: Washington, Simon Mannering, Fred (university Of S Название: Statistical and econometric methods for transportation data analysis ISBN: 0367199025 ISBN-13(EAN): 9780367199029 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies in various aspects of transportation planning, engineering, safety, and economics.
Автор: Asis Kumar Chattopadhyay; Tanuka Chattopadhyay Название: Statistical Methods for Astronomical Data Analysis ISBN: 1493915061 ISBN-13(EAN): 9781493915064 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis.
Описание: The purpose of this book is to provide an accessible, yet comprehensive, account of data science and machine learning. It is intended for anyone interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.
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