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Statistical Modeling using Local Gaussian Approximation extends
powerful characteristics of the Gaussian distribution, perhaps, the
most well-known and most used distribution in statistics, to a
large class of non-Gaussian and nonlinear situations through local
approximation. This extension enables the reader to follow new
methods in assessing dependence and conditional dependence, in
estimating probability and spectral density functions, and in
discrimination. Chapters in this release cover Parametric,
nonparametric, locally parametric, Dependence, Local Gaussian
correlation and dependence, Local Gaussian correlation and the
copula, Applications in finance, and more. Additional chapters
explores Measuring dependence and testing for independence, Time
series dependence and spectral analysis, Multivariate density
estimation, Conditional density estimation, The local Gaussian
partial correlation, Regression and conditional regression
quantiles, and a A local Gaussian Fisher discriminant.
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