Fractional iterated Ornstein-Uhlenbeck Processes
Juan Kalemkerian,José R. León +1 more
TLDR
This work presents a Gaussian process that arise from the iteration of p fractional Ornstein-Uhlenbeck processes generated by the same fractional Brownian motion, and proves that it results in a short memory processes.About:
This article is published in ALEA-Latin American Journal of Probability and Mathematical Statistics.The article was published on 2019-01-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Fractional Brownian motion.read more
Citations
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An independence test based on recurrence rates
Juan Kalemkerian,Diego Fernández +1 more
TL;DR: In this article, a new test of independence between random elements is presented, based on a functional of the Cramer-von Mises type, which is applied to a U -process that is defined from the recurrence rates.
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An Independence Test Based on Recurrence Rates. An empirical study and applications to real data.
TL;DR: From simulations, it is obtained that in high dimension, the independence test clearly outperforms, in almost all cases, the other widely used competitors.
Journal ArticleDOI
An independence test based on recurrence rates. An empirical study and applications to real data
TL;DR: In this paper , the authors proposed several variants to perform the independence test between two random elements based on recurrence rates and showed how to calculate the test statistic in each one of these cases.
Posted Content
An Independence Test Based on Recurrence Rates
Juan Kalemkerian,Diego Fernández +1 more
TL;DR: A new test of independence between random elements is presented, based on a functional of the Cramer-von Mises type, which is applied to a $U$-process that is defined from the recurrence rates.
Parameter estimation for the FOU(p) process with the same lambda
TL;DR: This work proposes a new method to estimate the unique value of λ in a FOU( p ) process and shows that this new method is more easy and fast to compute and is more efficient than the general method.
References
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Journal ArticleDOI
Time series analysis, forecasting and control
P. Young,S. Shellswell +1 more
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
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Introduction to Time Series and Forecasting.
TL;DR: A general approach to Time Series Modelling and ModeLLing with ARMA Processes, which describes the development of a Stationary Process in Terms of Infinitely Many Past Values and the Autocorrelation Function.
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Some Comments on the Evaluation of Model Performance
TL;DR: In this article, it is suggested that the correlation between model-predicted and observed data, commonly described by Pearson's productmoment correlation coefficient, is an insufficient and often misleading measure of accuracy, and a complement of difference and summary univariate indices is presented as the nucleus of a more informative, albeit fundamentally descriptive, approach to model evaluation.
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On the theory of brownian motion
TL;DR: The spectrum of the Fokker-Planck operator for weakly coupled gases is considered in this paper, where the operator is decomposed into operators acting on functions whose angular dependence is given by spherical harmonics.
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On the theory of brownian motion
TL;DR: In this article, it was shown that the non-Gaussian-Markoff process for Brownian motion derived on a statistical mechanical basis by Prigogine and Balescu, and Prigogueine and Philippot, is related through a transformation of variables to the Gaussian Markoff process of the conventional phenomenological theory of Brownian motions.