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OverviewStatistical 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. Full Product DetailsAuthor: Dag Tjøstheim (Emeritus Professor, Department of Mathematics, University of Bergen, Norway) , Håkon Otneim (Norwegian School of Economics, Bergen, Norway) , Bård Støve (Associate Professor, Department of Mathematics, University of Bergen, Norway)Publisher: Elsevier Science Publishing Co Inc Imprint: Academic Press Inc Weight: 0.700kg ISBN: 9780128158616ISBN 10: 0128158611 Pages: 458 Publication Date: 08 October 2021 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of Contents1. 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 discriminantReviewsAuthor InformationDag 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. 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. Tab Content 6Author Website:Countries AvailableAll regions |