Statistical Modeling Using Local Gaussian Approximation

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 conditional distribution functions, conditional mean functions and conditional quantile functions. Three R packages are integrated with the text, based on local Gaussian correlation, density and conditional density estimation, and local spectral analysis. The book is of particular relevance and interest to researchers in econometrics and financial econometrics. Reviews local dependence modelling 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