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This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness...Lees meer
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...Lees meer
Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately inten...Lees meer
Model-Free Curve Estimation details the Fourier series approach to density estimation and explores how model-free technology can be expanded to deal w...Lees meer
"Business, government, and industry all need efficient and accurate methods of summarizing and extracting information from the huge amounts of data co...Lees meer
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...Lees meer
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties conne...Lees meer
This book aims to steer the reader through a pool of techniques available for analysing repeated measurements. It describes an appropriately random se...Lees meer
The bestselling second edition of this outstanding text remains popular today for its clarity, richness of content and direct relevance to agricultura...Lees meer
The components of variance is a notion essential to statisticians and quantitative research scientists working in a variety of fields, including the b...Lees meer
Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields incl...Lees meer
Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states ...Lees meer
Written by pioneers in this exciting new field, Algebraic Statistics introduces the application of polynomial algebra to experimental design, discrete...Lees meer
Local Polynomial Modelling and Its Applications describes data-analytic approaches to regression problems encountered in many scientific disciplines. ...Lees meer
How can major corporations and governments more quickly and accurately detect and address cyberattacks on their networks? How can local authorities im...Lees meer
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula mo...Lees meer
Kernal Smoothing provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated b...Lees meer
Bovine Spongiform Encephalopathy (BSE) or "mad cow disease," first diagnosed in late 1986, is transmitted through feed, indirect horizontal transmissi...Lees meer
Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove o...Lees meer
This book describes the properties of stochastic probabilistic models and develops the applied mathematics of stochastic point processes. It is useful...Lees meer
Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy ...Lees meer
This book addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. It...Lees meer
Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique...Lees meer
Exploring the recent achievements that have occurred since the mid-1990s, this book explains how to use modern algorithms to fit geometric contours to...Lees meer