Автор: Tutz Название: Modeling Discrete Time-to-Event Data ISBN: 3319281569 ISBN-13(EAN): 9783319281568 Издательство: Springer Рейтинг: Цена: 8537.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.
Описание: This book shows how simulation can facilitate improvements on the job and in communities. The reader will learn to plan a project and communicate using a charter, how to establish simultaneous intervals on key responses and apply selection and ranking, and more.
Автор: Armin Zimmermann Название: Stochastic Discrete Event Systems ISBN: 3642093507 ISBN-13(EAN): 9783642093500 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Stochastic discrete-event systems (SDES) capture the randomness in choices due to activity delays and the probabilities of decisions. This book delivers a comprehensive overview on modeling with a quantitative evaluation of SDES. It presents an abstract model class for SDES as a pivotal unifying result and details important model classes.
Автор: Elashoff, Robert Название: Joint Modeling of Longitudinal and Time-to-Event Data ISBN: 0367570572 ISBN-13(EAN): 9780367570576 Издательство: Taylor&Francis Рейтинг: Цена: 8114.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Elashoff Название: Joint Modeling of Longitudinal and Time-to-Event Data ISBN: 1439807825 ISBN-13(EAN): 9781439807828 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues.
Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website.
This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.
Автор: Guerrero, Hector Название: Excel data analysis ISBN: 3030012786 ISBN-13(EAN): 9783030012786 Издательство: Springer Рейтинг: Цена: 12196.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive and readable introduction to modern business and data analytics.
Автор: Rakhee Kulshrestha Название: Mathematical modeling and computation of real-time problems ISBN: 0367517434 ISBN-13(EAN): 9780367517434 Издательство: Taylor&Francis Рейтинг: Цена: 26030.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers an interdisciplinary approach for understanding mathematical modeling by offering a collection of models, solved problems related to the models the methodologies employed, and the results using projects and case studies with insight into the operation of substantial real-time systems.
Автор: Ozaki Tohru Название: Time Series Modeling of Neuroscience Data ISBN: 1420094602 ISBN-13(EAN): 9781420094602 Издательство: Taylor&Francis Рейтинг: Цена: 26030.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.
Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include:
A statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more
Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike
A state space modeling method for dynamicization of solutions for the Inverse Problems
A heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis
An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series
An innovation-based method for spatial time series modeling for fMRI data analysis
The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role.
Описание: Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Описание: This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
Автор: Chi Название: Discrete-Time Adaptive Iterative Learning Control ISBN: 9811904669 ISBN-13(EAN): 9789811904660 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
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