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Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately inten...Savoir plus
This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness...Savoir plus
Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its wid...Savoir plus
Model-Free Curve Estimation details the Fourier series approach to density estimation and explores how model-free technology can be expanded to deal w...Savoir plus
"Business, government, and industry all need efficient and accurate methods of summarizing and extracting information from the huge amounts of data co...Savoir plus
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard...Savoir plus
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties conne...Savoir plus
This book aims to steer the reader through a pool of techniques available for analysing repeated measurements. It describes an appropriately random se...Savoir plus
The components of variance is a notion essential to statisticians and quantitative research scientists working in a variety of fields, including the b...Savoir plus
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, trea...Savoir plus
Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields incl...Savoir plus
Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states ...Savoir plus
Written by pioneers in this exciting new field, Algebraic Statistics introduces the application of polynomial algebra to experimental design, discrete...Savoir plus
How can major corporations and governments more quickly and accurately detect and address cyberattacks on their networks? How can local authorities im...Savoir plus
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula mo...Savoir plus
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric m...Savoir plus
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local averaging or smoo...Savoir plus
Bovine Spongiform Encephalopathy (BSE) or "mad cow disease," first diagnosed in late 1986, is transmitted through feed, indirect horizontal transmissi...Savoir plus
Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove o...Savoir plus
This book describes the properties of stochastic probabilistic models and develops the applied mathematics of stochastic point processes. It is useful...Savoir plus
Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy ...Savoir plus
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is de...Savoir plus
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph con...Savoir plus
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as gene...Savoir plus