Probability and statistics

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis.

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors.

Statistical learning theory is aimed at analyzing complex data with necessarily approximate models.

This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory.

Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas.

Computer science and physics have been closely linked since the birth of modern computing.

This book discusses the multifaceted nature of organizational excellence and proceeds to consider the roles played by data, technological advances leading to innovation and value-based leadership in guiding an organization in its journey towards excellence.

From 1972 to 1974, I was working on a PhD thesis entitled Multiple Server Queues with Service Time Depending on Waiting Time.

Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed.

The current financial crisis has revealed serious flaws in models, measures and, potentially, theories, that failed to provide forward-looking expectations for upcoming losses originated from market risks.

The reproducing kernel Hilbert space construction is a bijection or transform theory which associates a positive definite kernel (gaussian processes) with a Hilbert space offunctions.

Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information.

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice.

Unlike the first edition, the new edition has been split into two books.

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models.

This book gives the basis of the probabilistic functional analysis on Wiener space, developed during the last decade.

Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis provides statisticians with an understanding of the critical challenges currently encountered in oncology trials.

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field.

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.

Dieses Lehrbuch gibt einen umfassenden Überblick über Methoden der deskriptiven Statistik, die durch einige Verfahren der explorativen Datenanalyse ergänzt wurden.

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples.

In this book, the author rejects the theorem-proof approach as much as possible, and emphasize the practical application of econometrics.

This contributed volume features invited papers on current models and statistical methods for spatial and multivariate data.

Factorial designs enable researchers to experiment with many factors.

This book addresses the problem of multi-agent systems, considering that it can be interpreted as a generalized multi-synchronization problem.

Statistical distributions are one of the most important applied mathematical tools across a wide spectrum of disciplines, including engineering, biological sciences, and health and social sciences.

Poverty and inequality remain at the top of the global economic agenda, and the methodology of measuring poverty continues to be a key area of research.

Mathematicians call it the Monty Hall Problem, and it is one of the most interesting mathematical brain teasers of recent times.

This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics.

This book presents the Proceedings of The 6th Brazilian Technology Symposium (BTSym'20).

3D rotation analysis is widely encountered in everyday problems thanks to the development of computers.

This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis.

This textbook offers a comprehensive analysis of medical decision making under uncertainty by combining Test Information Theory with Expected Utility Theory.

Master the essential mathematical tools required for option pricing within the context of a specific, yet fundamental, pricing model.

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Optimal Decisions: Principles of Programming deals with all important problems related to programming.

Praise for the first edition:"e;One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff-inferential statistics.

There has been an increase in awareness (and perhaps occurrence) of individual and organized cheating on tests.

This is the first volume to present a comprehensive treatment of the theory and application of life table techniques.

This book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations.

Introducing gravitational-wave data analysis, this is an ideal starting point for researchers entering the field, and researchers currently analyzing data.

This book explores key examples concerning the implementation of information technology and mathematical modeling to solve issues concerning environmental sustainability.

This book, which focuses on the study of curvature, is an introduction to various aspects of pseudo-Riemannian geometry.

"e;Algorithmic information theory (AIT) is the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously"e;, says G.

The book presents a general mathematical framework able to detect and to characterise, from a morphological and statistical perspective, patterns hidden in spatial data.

This book aims to help the reader better understand the importance of data analysis in project management.

It is a great privilege and pleasure to write a foreword for a book honor- ing Wolfgang Gaul on the occasion of his sixtieth birthday.