A central limit theorem for random fields.

Fazekas, István; Chuprunov, Alexey

Displaying similar documents to “A central limit theorem for random fields.”

One Bootstrap suffices to generate sharp uniform bounds in functional estimation

Paul Deheuvels (2011)

Kybernetika

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We consider, in the framework of multidimensional observations, nonparametric functional estimators, which include, as special cases, the Akaike–Parzen–Rosenblatt kernel density estimators ([1, 18, 20]), and the Nadaraya–Watson kernel regression estimators ([16, 22]). We evaluate the sup-norm, over a given set $𝐈$ , of the difference between the estimator and a non-random functional centering factor (which reduces to the estimator mean for kernel density estimation). We show that, under...

Drift estimation from $\tilde{ρ}$ -mixing sequences.

Arfi, Mounir (2008)

International Journal of Open Problems in Computer Science and Mathematics. IJOPCM

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Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations.

Vasil&#039;ev, V.A., Koshkin, G.M. (2000)

Siberian Mathematical Journal

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An efficient estimator for Gibbs random fields

Martin Janžura (2014)

Kybernetika

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An efficient estimator for the expectation $\int f P ̣$ is constructed, where $P$ is a Gibbs random field, and $f$ is a local statistic, i. e. a functional depending on a finite number of coordinates. The estimator coincides with the empirical estimator under the conditions stated in Greenwood and Wefelmeyer [6], and covers the known special cases, namely the von Mises statistic for the i.i.d. underlying fields and the case of one-dimensional Markov chains.

Sample behavior and laws of large numbers for Gaussian random elements.

Ergemlidze, Z., Shangua, A., Tarieladze, V. (2003)

Georgian Mathematical Journal

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Asymptotic behaviour of a transport equation

Ryszard Rudnicki (1992)

Annales Polonici Mathematici

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We study the asymptotic behaviour of the semigroup of Markov operators generated by the equation $u_{t} + b u_{x} + c u = a \int_{0}^{a x} u (t, a x - y) μ (d y)$ . We prove that for a > 1 this semigroup is asymptotically stable. We show that for a ≤ 1 this semigroup, properly normalized, converges to a limit which depends only on a.

On bounds for the characteristic functions of some degenerate multidimensional distributions.

Shervashidze, T. (2003)

Georgian Mathematical Journal

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Asymptotics for weakly dependent errors-in-variables

Michal Pešta (2013)

Kybernetika

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Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. Weakly dependent ( $α$ - and $ϕ$ -mixing) disturbances, which are not necessarily stationary...

On the strong convergence for weighted sums of asymptotically almost negatively associated random variables

Haiwu Huang, Guangming Deng, QingXia Zhang, Yuanying Jiang (2014)

Kybernetika

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Applying the moment inequality of asymptotically almost negatively associated (AANA, in short) random variables which was obtained by Yuan and An (2009), some strong convergence results for weighted sums of AANA random variables are obtained without assumptions of identical distribution, which generalize and improve the corresponding ones of Zhou et al. (2011), Sung (2011, 2012) to the case of AANA random variables, respectively.