Statistical Modeling Using Local Gaussian Approximation

Erscheinungsjahr 2021
ISBN: 978-0-12-815861-6
Verlag: William Andrew Publishing

Buch, Englisch, 458 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 700 g

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.

Zielgruppe

<p>Graduate students and 1<SUP>st</SUP> year PhD students researching problems in econometrics, statistics, fi</p> <p>nancial econometrics and related areas where it is important to model statistical dependence, do density and conditional density estimation, and seek for periodicities and cycles in data.</p>

Autoren/Hrsg.

Tjøstheim, Dag

Otneim, Håkon

Støve, Bård

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

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

Über Autor(innen)

Otneim, Håkon
Håkon Otneim is Associate Professor at the Norwegian School of Economics. He has a PhD in statistics from the University of Bergen (2016). He has published papers in international journals about multivariate density estimation and conditional density estimation. His research interests include development and application of non- and semiparametric statistics, statistical programming, and data visualization.

Støve, Bård
Bård Støve is Professor of Statistics at the University of Bergen. He received his PhD degree in Statistics, 2005. He was Assistant Professor at the Norwegian School of Economics (2007--2011) and worked as an Actuary in a consulting firm (2005--2007). He has been working on the development of nonparametric models and application of such models to areas in finance and economics. He has published several research papers in journals such as Econometric Theory and Scandinavian Journal of Statistics.

Tjøstheim, Dag
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.