Statistical Modeling Using Local Gaussian Approximation
-
- Taschenbuch
- eBook ausgewählt
-
Form:Einzelkauf Download
-
Sprache:Englisch
109,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
05.10.2021
Verlag
Elsevier Science & Techn.Seitenzahl
458 (Printausgabe)
Dateigröße
24570 KB
Sprache
Englisch
EAN
9780128154458
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
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
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.
Kurze Frage zu unserer Seite
Vielen Dank für Ihr Feedback
Wir nutzen Ihr Feedback, um unsere Produktseiten zu verbessern. Bitte haben Sie Verständnis, dass wir Ihnen keine Rückmeldung geben können. Falls Sie Kontakt mit uns aufnehmen möchten, können Sie sich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice