Partially Linear Models, Wolfgang H?rdle; Hua Liang; Jiti Gao
Автор: Agamirza E. Bashirov Название: Partially Observable Linear Systems Under Dependent Noises ISBN: 3034894074 ISBN-13(EAN): 9783034894074 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Noise is a rich concept playing an underlying role in human activity. Consideration of the noise phenomenon in arts and sciences, respectively, makes the distinction between both domains more obvious. Artists create "deliberate noise" ; the masterpieces of literature, music, modern fine art etc. are those where a clear idea, traditionally related to such concepts as love, is presented under a skilful veil of "deliberate noise." On the contrary, sciences fight against noise; a scientific discovery is a law of nature extracted from a noisy medium and refined.
This book discusses the methods of fighting against noise. It can be regarded as a mathematical view of specific engineering problems with known and new methods of control and estimation in noisy media.
The main feature of this book is the investigation of stochastic optimal control and estimation problems with the noise processes acting dependently on the state (or signal) and observation systems. While multiple early and recent findings on the subject have been obtained and challenging problems remain to be solved, this subject has not yet been dealt with systematically nor properly investigated. The discussion is given for infinite dimensional systems, but within the linear quadratic framework for continuous and finite time horizon. In order to make this book self-contained, some background material is provided.
Consequently, the target readers of this book are both applied mathematicians and theoretically oriented engineers who are designing new technology, as well as students of the related branches. The book may also be used as a reference manual in that part of functional analysis that is needed for problems of infinite dimensional linear systems theory.
Автор: Massih-Reza Amini; Nicolas Usunier Название: Learning with Partially Labeled and Interdependent Data ISBN: 331935390X ISBN-13(EAN): 9783319353906 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data.
Автор: Douglas E. Critchlow Название: Metric Methods for Analyzing Partially Ranked Data ISBN: 0387962883 ISBN-13(EAN): 9780387962887 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A full ranking of n items is simply an ordering of all these items, of the form: first choice, second choice, *. The problem thus becomes one of ex- tending metrics on the permutation group to metrics on a coset space of the permutation group.
Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.
Автор: Martin A Hjortso Название: Linear Mathematical Models In Chemical Engineering ISBN: 9812794158 ISBN-13(EAN): 9789812794154 Издательство: World Scientific Publishing Рейтинг: Цена: 16790.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reviews, introduces, and develops the mathematics that is encountered in sophisticated chemical engineering models. This book provides coverage of chemical engineering model formulation and analysis. It serves as an introduction to linear mathematics for engineering students.
Автор: Gruber Marvin H J Название: Linear Models ISBN: 1118952839 ISBN-13(EAN): 9781118952832 Издательство: Wiley Рейтинг: Цена: 20109.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book more reader friendly than the first edition.
Автор: Fox John Название: Applied Regression Analysis and Generalized Linear Models ISBN: 1452205663 ISBN-13(EAN): 9781452205663 Издательство: Sage Publications Рейтинг: Цена: 25027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.
Автор: R. Conte; N. Boccara Название: Partially Integrable Evolution Equations in Physics ISBN: 9401067546 ISBN-13(EAN): 9789401067546 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proceedings of the NATO Advanced Study Institute on Partially Integrable Nonlinear Evolution Equations and Their Physical Applications, Les Houches, France, March 21-30, 1989
Описание: This text lays algebraic foundations for real geometry through a systematic investigation of partially ordered rings of semi-algebraic functions. It also covers topics such as ordered algebraic structures, topology and rings of continuous functions.
Автор: Faraway Julian J Название: Linear models with R ISBN: 1439887330 ISBN-13(EAN): 9781439887332 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Нет в наличии.
Описание:
A Hands-On Way to Learning Data Analysis
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality
Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates
Extensive use of the ggplot2 graphics package in addition to base graphics
Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
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