Statistical Modeling Using Local Gaussian Approximation (eBook)

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. - Reviews local dependence modeling with applications to time series and finance markets - Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics - Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences - Integrates textual content with three useful R packages

Dag Tjøstheim is Emeritus Professor, Department of Mathematics, University of Bergen. He has a PhD in applied mathematics from Princeton University (1974). He has authored more than 120 papers in international journals. He is a member of the Norwegian Academy of Sciences and has received several prizes for his scientific work. His main interests are in econometrics, nonlinear time series, nonparametric methods, modeling of dependence, spatial variables, and fishery statistics.

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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. - Reviews local dependence modeling with applications to time series and finance markets- Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics- Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences- Integrates textual content with three useful R packages