Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Tatiana Miazhynskaia"'
Publikováno v:
Neural Computation. 20(2):504-522
We use neural networks (NN) as a tool for a nonlinear autoregression to predict the second moment of the conditional density of return series. The NN models are compared to the popular econometric GARCH(1,1) model. We estimate the models in a Bayesia
Publikováno v:
Computational Statistics & Data Analysis. 51:2029-2042
Neural networks provide a tool for describing non-linearity in volatility processes of financial data and help to answer the question ''how much'' non-linearity is present in the data. Non-linearity is studied under three different specifications of
Autor:
Tatiana Miazhynskaia, Georg Dorffner
Publikováno v:
Statistical Papers. 47:525-549
This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class o
Publikováno v:
Adaptive Information Systems and Modelling in Economics and Management Science ISBN: 9783211206843
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b81c86b19bf5b0a838e41696b63083d
https://doi.org/10.1007/3-211-29901-7_5
https://doi.org/10.1007/3-211-29901-7_5
Publikováno v:
SSRN Electronic Journal.
This study evaluates a set of parametric and non-parametric value-at-risk (VaR) models that quantify the uncertainty in VaR estimates in form of a VaR distribution. We propose a new VaR approach based on Bayesian statistics in a GARCH volatility mode
Autor:
B. Kemp, Peter Rappelsberger, Josef Zeitlhofer, Sari-Leena Himanen, Georg Gruber, Gerhard Klösch, Dieter Kunz, M.J. Barbanoj, Heidi Danker-Hopfe, Silvia Parapatics, Tatiana Miazhynskaia, Bernd Saletu, Thomas Penzel, Peter Anderer, Alois Schlögl, Michael Woertz, Michael Grözinger, Georg Dorffner
Publikováno v:
Neuropsychobiology. 51(3)
To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification proce