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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
This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness...Lees meer
Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possib...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
Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields incl...Lees meer
Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials an...Lees meer
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, trea...Lees meer
Repeated measures data arise when the same characteristic is measured on each case or subject at several times or under several conditions. There is a...Lees meer
Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states ...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
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local averaging or smoo...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
Written by pioneers in this exciting new field, Algebraic Statistics introduces the application of polynomial algebra to experimental design, discrete...Lees meer
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric m...Lees meer
Bovine Spongiform Encephalopathy (BSE) or "mad cow disease," first diagnosed in late 1986, is transmitted through feed, indirect horizontal transmissi...Lees meer
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...Lees meer
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...Lees meer
Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy ...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
Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These meth...Lees meer