Statistical Modeling Using Local Gaussian Approximation

Helps econometricians and applied statisticians obtain reliable results in nonlinear dependence and density modeling

Dag Tjøstheim (Author), Håkon Otneim (Author), Bård Støve (Author)

9780128158616, Elsevier Science

Paperback, published 8 October 2021

458 pages
22.9 x 15.2 x 2.9 cm, 0.7 kg

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.

1. Introduction
2. Parametric, nonparametric, locally parametric
3. Dependence
4. Local Gaussian correlation and dependence
5. Local Gaussian correlation and the copula
6. Applications in finance
7. Measuring dependence and testing for independence
8. Time series dependence and spectral analysis
9. Multivariate density estimation
10. Conditional density estimation
11. The local Gaussian partial correlation
12. Regression and conditional regression quantiles
13. A local Gaussian Fisher discriminant

Subject Areas: Business mathematics & systems [KJQ]