Displaying similar documents to “Efficient measurement of higher-order statistics of stochastic processes”

Study with George Styan

Philip V. Bertrand (2013)

Discussiones Mathematicae Probability and Statistics

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George Styan and I first met when he came to the University of Birmingham to study Mathematics. We both developed interests in Mathematical Statistics. Matrix theory is an essential component of such courses and we both excelled at it. Our paths have intersected on a number of occasions and George's insight and cooperation has been a great help in enabling me to carry out research in several areas. This article summarizes the value of George's presence and help in carrying out some interesting...

Numerical optimization of parameters in systems of differential equations

Martínek, Josef, Kučera, Václav

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We present results on the estimation of unknown parameters in systems of ordinary differential equations in order to fit the output of models to real data. The numerical method is based on the nonlinear least squares problem along with the solution of sensitivity equations corresponding to the differential equations. We will present the performance of the method on the problem of fitting the output of basic compartmental epidemic models to data from the Covid-19 epidemic. This allows...

Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process

S. Valère Bitseki Penda, Hacène Djellout, Frédéric Proïa (2014)

ESAIM: Probability and Statistics

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The purpose of this paper is to investigate moderate deviations for the Durbin–Watson statistic associated with the stable first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We first establish a moderate deviation principle for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise. It enables us to provide a moderate...

Canonical input-output representation of linear multivariable stochastic systems and joint optimal parameter and state estimation.

G. Salut, J. Aguilar-Martín, S. Lefevre (1979)

Stochastica

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In this paper a complete presentation is given of a new canonical representation of multi-input, multi-output linear stochastic systems. Its equivalence with operator form directly linked with ARMA processes as well as with classical state space representation is given, and a transfer matrix interpretation is developed in an example. The importance of the new representation is mainly in the fact that in the joint state and parameters estimation problem, all unknown parameters appear...