Statistical Modeling using Local Gaussian Approximationextends 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. Reviews local dependence modeling with applications to time series and finance marketsIntroduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economicsEvaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differencesIntegrates textual content with three useful R packages | Statistical Modeling Using Local Gaussian Approximation by Dag Tjostheim, Paperback | Indigo Chapters