The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Veli Bayramov - One of the best experts on this subject based on the ideXlab platform.
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on the asymptotic behaviour of the Covariance Function of the rewards of a multivariate renewal reward process
Statistics & Probability Letters, 2017Co-Authors: Rovshan Aliyev, Veli BayramovAbstract:Abstract In this study, a renewal–reward process with multivariate rewards is investigated and an asymptotic expansion for the Covariance Function of the rewards is derived. The remainder term of the result in Patch et al. (2015) is sharpened from the o ( 1 ) to o ( t − k ) . Moreover, asymptotic expansions as t → ∞ with the remainder term o ( t − k ) for the mathematical expectation and variance of the renewal–reward process also are obtained.
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On the asymptotic behaviour of the Covariance Function of the rewards of a multivariate renewal–reward process
Statistics & Probability Letters, 2017Co-Authors: Rovshan Aliyev, Veli BayramovAbstract:Abstract In this study, a renewal–reward process with multivariate rewards is investigated and an asymptotic expansion for the Covariance Function of the rewards is derived. The remainder term of the result in Patch et al. (2015) is sharpened from the o ( 1 ) to o ( t − k ) . Moreover, asymptotic expansions as t → ∞ with the remainder term o ( t − k ) for the mathematical expectation and variance of the renewal–reward process also are obtained.
Yury V. Kozachenko - One of the best experts on this subject based on the ideXlab platform.
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Construction of a criterion for testing hypothesis about Covariance Function of a stationary Gaussian stochastic process with unknown mean
Communications in Statistics - Theory and Methods, 2017Co-Authors: Yury V. Kozachenko, Viktor B. TroshkiAbstract:In the paper, a new criterion is constructed for testing hypothesis about Covariance Function of Gaussian stationary stochastic process with an unknown mean. This criterion is based on the fact, that we can estimate the deviation of Covariance Function from its estimator with a given accuracy and reliability in Lp metric.
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a criterion for testing hypotheses about the Covariance Function of a stationary gaussian stochastic process
Modern Stochastics: Theory and Applications, 2015Co-Authors: Yury V. Kozachenko, Viktor B. TroshkiAbstract:We consider a measurable stationary Gaussian stochastic process. A criterion for testing hypotheses about the Covariance Function of such a process using estimates for its norm in the space $L_p(\mathbb {T}),\, p\geq1$, is constructed.
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the criterion of hypothesis testing on the Covariance Function of a gaussian stochastic process
Monte Carlo Methods and Applications, 2014Co-Authors: Yury V. Kozachenko, Mykola SergiienkoAbstract:We consider a square Gaussian stochastic process. Estimates of the distribution of some Functional of this process are obtained. Tests for a hypothesis concerning the form of the Covariance Function of a Gaussian stochastic process are constructed.
Mykola Sergiienko - One of the best experts on this subject based on the ideXlab platform.
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the criterion of hypothesis testing on the Covariance Function of a gaussian stochastic process
Monte Carlo Methods and Applications, 2014Co-Authors: Yury V. Kozachenko, Mykola SergiienkoAbstract:We consider a square Gaussian stochastic process. Estimates of the distribution of some Functional of this process are obtained. Tests for a hypothesis concerning the form of the Covariance Function of a Gaussian stochastic process are constructed.
Rovshan Aliyev - One of the best experts on this subject based on the ideXlab platform.
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on the asymptotic behaviour of the Covariance Function of the rewards of a multivariate renewal reward process
Statistics & Probability Letters, 2017Co-Authors: Rovshan Aliyev, Veli BayramovAbstract:Abstract In this study, a renewal–reward process with multivariate rewards is investigated and an asymptotic expansion for the Covariance Function of the rewards is derived. The remainder term of the result in Patch et al. (2015) is sharpened from the o ( 1 ) to o ( t − k ) . Moreover, asymptotic expansions as t → ∞ with the remainder term o ( t − k ) for the mathematical expectation and variance of the renewal–reward process also are obtained.
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On the asymptotic behaviour of the Covariance Function of the rewards of a multivariate renewal–reward process
Statistics & Probability Letters, 2017Co-Authors: Rovshan Aliyev, Veli BayramovAbstract:Abstract In this study, a renewal–reward process with multivariate rewards is investigated and an asymptotic expansion for the Covariance Function of the rewards is derived. The remainder term of the result in Patch et al. (2015) is sharpened from the o ( 1 ) to o ( t − k ) . Moreover, asymptotic expansions as t → ∞ with the remainder term o ( t − k ) for the mathematical expectation and variance of the renewal–reward process also are obtained.
Subba T Rao - One of the best experts on this subject based on the ideXlab platform.
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a new Covariance Function and spatio temporal prediction kriging for a stationary spatio temporal random process
Journal of Time Series Analysis, 2017Co-Authors: Subba T Rao, Gyorgy TerdikAbstract:Consider a stationary spatio-temporal random process Yts;s∈Rd,t∈Z and let Ytsi;i=1,2,…,m;t=1,…,n be a sample from the process. Our object here is to predict, given the sample, Ytso for all t at the location so. To obtain the predictors, we define a sequence of discrete Fourier transforms Jsiωj;i=1,2,…,m using the observed time series. We consider these discrete Fourier transforms as a sample from the complex valued random variable Jsω. Assuming that the discrete Fourier transforms satisfy a complex stochastic partial differential equation of the Laplacian type with a scaling Function that is a polynomial in the temporal spectral frequency ω, we obtain, in a closed form, expressions for the second-order spatio-temporal spectrum and the Covariance Function. The spectral density Function obtained corresponds to a non-separable random process. The optimal predictor of the discrete Fourier transform Jsoω is in terms of the Covariance Functions. The estimation of the parameters of the spatio-temporal Covariance Function is considered and is based on the recently introduced frequency variogram method. The methods given here can be extended to situations where the observations are corrupted by independent white noise. The methods are illustrated with a real data set.
