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...