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: 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: Clarke, Bertrand S. (university Of Nebraska, Lincoln) Clarke, Jennifer L. (university Of Nebraska, Lincoln) Название: Predictive statistics ISBN: 1107028280 ISBN-13(EAN): 9781107028289 Издательство: Cambridge Academ Рейтинг: Цена: 12514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and `black box` settings, with computed examples.
Описание: This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.The book is divided into five main sections:* Binomial statistical inference;* Statistical inference by inverse probability;* The central limit theorem and linear minimum variance estimation by Laplace and Gauss;* Error theory, skew distributions, correlation, sampling distributions;* The Fisherian Revolution, 1912-1935.Throughout each of the chapters, the author provides lively biographical sketches of many of the main characters, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. He also examines the roles played by DeMoivre, James Bernoulli, and Lagrange, and he provides an accessible exposition of the work of R.A. Fisher.This book will be of interest to statisticians, mathematicians, undergraduate and graduate students, and historians of science.
Автор: Geisser, Seymour Название: Predictive Inference ISBN: 0367449919 ISBN-13(EAN): 9780367449919 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents Seymour Geisser`s views on predictive or observable inference and its advantages over parametric inference. It focuses on the predictive applications of the Bayesian approach. The book also presents predictive analyses that have no real parametric analogues.
Автор: Wasserman, Larry Название: All of statistics: A Concise Course in Statistical Inference ISBN: 1441923225 ISBN-13(EAN): 9781441923226 Издательство: Springer Рейтинг: Цена: 7965.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Автор: Juditsky Anatoli, Nemirovski Arkadi Название: Statistical Inference Via Convex Optimization ISBN: 0691197296 ISBN-13(EAN): 9780691197296 Издательство: Wiley Рейтинг: Цена: 13939.00 р. Наличие на складе: Поставка под заказ.
Описание:
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences.
Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems--sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals--demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.
Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.
A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field
This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided.
The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.
Presented in three parts--Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics--Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.
Автор: Wook Hyun Kwon; PooGyeon Park Название: Stabilizing and Optimizing Control for Time-Delay Systems ISBN: 3030064964 ISBN-13(EAN): 9783030064969 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Нет в наличии.
Описание: Stabilizing and Optimizing Control for Time-Delay Systems introduces three important classes of stabilizing controls for time-delay systems: non-optimal (without performance criteria); suboptimal (including guaranteed costs); and optimal controls. Each class is treated in detail and compared in terms of prior control structures. State- and input-delayed systems are considered. The book provides a unified mathematical framework with common notation being used throughout.Receding-horizon, or model predictive, linear quadratic (LQ), linear-quadratic-Gaussian and H? controls for time-delay systems are chosen as optimal stabilizing controls. Cost monotonicity is investigated in order to guarantee the asymptotic stability of closed-loop systems operating with such controls.The authors use guaranteed LQ and H? controls as representative sub-optimal methods; these are obtained with pre-determined control structures and certain upper bounds of performance criteria. Non-optimal stabilizing controls are obtained with predetermined control structures but with no performance criteria. Recently developed inequalities are exploited to obtain less conservative results.To facilitate computation, the authors use linear matrix inequalities to represent gain matrices for non-optimal and sub-optimal stabilizing controls, and all the initial conditions of coupled differential Riccati equations of optimal stabilizing controls. Numerical examples are provided with MATLAB® codes (downloadable from http://extras.springer.com/) to give readers guidance in working with more difficult optimal and suboptimal controls.Academic researchers studying control of a variety of real processes in chemistry, biology, transportation, digital communication networks and mechanical systems that are subject to time delays will find the results presented in Stabilizing and Optimizing Control for Time-Delay Systems to be helpful in their work. Practitioners working in related sectors of industry will also find this book to be of use in developing real-world control systems for the many time-delayed processes they encounter.
Автор: Biecek, Przemyslaw , Burzykowski, Tomasz Название: Predictive Models ISBN: 0367135590 ISBN-13(EAN): 9780367135591 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is about a new field in statistical machine learning - about interpretation and explanation of predictive models. Machine learning models are widely used in predictive modelling, both for regression and classification.
Автор: Nijjer, Shivinder (chitkara Business School, Punjab, India) Raj, Sahil (punjabi University, Patiala, Punjab, India) Название: Predictive analytics in human resource management ISBN: 0367460866 ISBN-13(EAN): 9780367460860 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume is a step by step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organizational impact, to aid in strategizing and better decision making.
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