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Estimates for simple random walks on fundamental groups of surfaces

Laurent Bartholdi, Serge Cantat, Tullio Ceccherini-Silberstein, Pierre de la Harpe (1997)

Colloquium Mathematicae

Numerical estimates are given for the spectral radius of simple random walks on Cayley graphs. Emphasis is on the case of the fundamental group of a closed surface, for the usual system of generators.

Estimating an even spherical measure from its sine transform

Lars Michael Hoffmann (2009)

Applications of Mathematics

To reconstruct an even Borel measure on the unit sphere from finitely many values of its sine transform a least square estimator is proposed. Applying results by Gardner, Kiderlen and Milanfar we estimate its rate of convergence and prove strong consistency. We close this paper by giving an estimator for the directional distribution of certain three-dimensional stationary Poisson processes of convex cylinders which have applications in material science.

Estimating the conditional expectations for continuous time stationary processes

Gusztáv Morvai, Benjamin Weiss (2020)

Kybernetika

One of the basic estimation problems for continuous time stationary processes X t , is that of estimating E { X t + β | X s : s [ 0 , t ] } based on the observation of the single block { X s : s [ 0 , t ] } when the actual distribution of the process is not known. We will give fairly optimal universal estimates of this type that correspond to the optimal results in the case of discrete time processes.

Estimation for heavy tailed moving average process

Hakim Ouadjed, Tawfiq Fawzi Mami (2018)

Kybernetika

In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables innovations...

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