Displaying similar documents to “How the maximum of gaussian random walks and fields is influenced by changes of the variances.”

On the measurement of the activity of a radioactive source and a related stochastic process.

J. M. F. Chamayou (1981)

Stochastica

Similarity:

A method is presented to compute the activity of a radioactive source. The principle of the method is based on the tuning of b, the time constant of the RC circuit of the detector with l being the rate of emission of the source, using a statistical argument. The stochastical process involved refers to the distribution of the following random voltage: Vt = ∑(0 < ti ≤ t) Yi c-b(t...

On m-dimensional stochastic processes in Banach spaces.

Rodolfo De Dominicis, Elvira Mascolo (1981)

Stochastica

Similarity:

In the present paper the authors prove a weak law of large numbers for multidimensional processes of random elements by means of the random weighting. The results obtained generalize those of Padgett and Taylor.

On independence in some families of multivariate distributions.

José Juan Quesada (1986)

Stochastica

Similarity:

In this paper we will prove a characterization for the independence of random vectors with positive (negative) orthant dependence according to a direction. The result can be seen as a generalization of a result by Lehmann [4].

Approximate solutions of matrix differential equations.

Lucas Jódar Sánchez, A. Hervás, D. García Sala (1986)

Stochastica

Similarity:

A method for solving second order matrix differential equations avoiding the increase of the dimension of the problem is presented. Explicit approximate solutions and an error bound of them in terms of data are given.

Algebraic methods for solving boundary value problems.

Lucas Jódar Sánchez (1986)

Stochastica

Similarity:

By means of the reduction of boundary value problems to algebraic ones, conditions for the existence of solutions and explicit expressions of them are obtained. These boundary value problems are related to the second order operator differential equation X + AX + AX = 0, and X = A + BX + XC. For the finite-dimensional case, computable expressions of the solutions are given.

Prediction of time series by statistical learning: general losses and fast rates

Pierre Alquier, Xiaoyin Li, Olivier Wintenberger (2013)

Dependence Modeling

Similarity:

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates...

Integral equations and time varying linear systems.

Lucas Jódar (1986)

Stochastica

Similarity:

In this paper we study the resolution problem of an integral equation with operator valued kernel. We prove the equivalence between this equation and certain time varying linear operator system. Sufficient conditions for solving the problem and explicit expressions of the solutions are given.

On measures of concordance.

Marco Scarsini (1984)

Stochastica

Similarity:

We give a general definition of concordance and a set of axioms for measures of concordance. We then consider a family of measures satisfying these axioms. We compare our results with known results, in the discrete case.