Statistical Modeling Using Local Gaussian Approximation
Language: English
Published by Academic Press, 2021
- Softcover
- New
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In English.
Seller Inventory # ria9780128158616_new
- Title
- Statistical Modeling Using Local Gaussian Approximation
- Author
- Tjøstheim, Dag; Otneim, Håkon; Støve, Bård
- Publisher
- Academic Press
- Publication year
- 2021
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0128158611
- ISBN 13
- 9780128158616
- Item weight
- 856 grams
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
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.
"About the title" may belong to another edition of this title.
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