Finite Gaussian mixture approximations to analytically intractable density Kernels
Natalia Khorunzhina, Jean-François Richard
| Year of publication: |
2019
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|---|---|
| Authors: | Khorunzhina, Natalia ; Richard, Jean-François |
| Published in: |
Computational economics. - Dordrecht [u.a.] : Springer, ISSN 0927-7099, ZDB-ID 1142021-2. - Vol. 53.2019, 3, p. 991-1017
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| Subject: | Adaptive algorithm | Density kernel | Distance measure | Finite mixture | Gaussian quadrature | Importance sampling | Stochastic volatility | Statistische Verteilung | Statistical distribution | Stochastischer Prozess | Stochastic process | Stichprobenerhebung | Sampling | Volatilität | Volatility | Schätztheorie | Estimation theory | Monte-Carlo-Simulation | Monte Carlo simulation | Optionspreistheorie | Option pricing theory | Algorithmus | Algorithm | Nichtparametrisches Verfahren | Nonparametric statistics |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s10614-017-9777-2 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012135106