Confidence bands for spectral characteristics [Abstract of thesis]
Confidence intervals for the capability indices and in stationary Caussian processes.
Conjugate priors for exponential-type processes with random initial conditions
The family of proper conjugate priors is characterized in a general exponential model for stochastic processes which may start from a random state and/or time.
Consistency and Asymptotic Normality of Maximum Likelihood Estimation for Gaussian Markov Processes from Discrete Obersvations.
Consistency of Estimators of Cyclic Functional Parameters for Some Nonstationary Processes
Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors
The least squres invariant quadratic estimator of an unknown covariance function of a stochastic process is defined and a sufficient condition for consistency of this estimator is derived. The mean value of the observed process is assumed to fulfil a linear regresion model. A sufficient condition for consistency of the least squares estimator of the regression parameters is derived, too.
Consistency of the BIC order estimator.
Consistent non-parametric bayesian estimation for a time-inhomogeneous brownian motion
We establish posterior consistency for non-parametric Bayesian estimation of the dispersion coefficient of a time-inhomogeneous Brownian motion.
Construction automatique et continue de prévisions
Contiguity and LAN-property of sequences of Poisson processes
Using the concept of Hellinger integrals, necessary and sufficient conditions are established for the contiguity of two sequences of distributions of Poisson point processes with an arbitrary state space. The distribution of logarithm of the likelihood ratio is shown to be infinitely divisible. The canonical measure is expressed in terms of the intensity measures. Necessary and sufficient conditions for the LAN-property are formulated in terms of the corresponding intensity measures.
Contribution pour servir à l'étude du choix que fit A. A. Markov d'un domaine d'application de sa théorie des chaînes
Control of uncertain processes: applied theory and algorithms
Convergence des surmartingales. Application aux vraisemblances partielles
Convergence en loi des H-variations d'un processus gaussien stationnaire sur R
Convergence model of interest rates of CKLS type
This paper deals with convergence model of interest rates, which explains the evolution of interest rate in connection with the adoption of Euro currency. Its dynamics is described by two stochastic differential equations – the domestic and the European short rate. Bond prices are then solutions to partial differential equations. For the special case with constant volatilities closed form solutions for bond prices are known. Substituting its constant volatilities by instantaneous volatilities we...
Copula approach to residuals of regime-switching models
The autocorrelation function describing the linear dependence is not suitable for description of residual dependence of the regime-switching models. In this contribution, inspired by Rakonczai ([20]), we will model the residual dependence of the regime-switching models (SETAR, LSTAR and ESTAR) with the autocopulas (Archimedean, EV and their convex combinations) and construct improved quality models for the original real time series.
Covariance structure of wide-sense Markov processes of order k ≥ 1
A notion of a wide-sense Markov process of order k ≥ 1, , is introduced as a direct generalization of Doob’s notion of wide-sense Markov process (of order k=1 in our terminology). A base for investigation of the covariance structure of is the k-dimensional process . The covariance structure of is considered in the general case and in the periodic case. In the general case it is shown that iff is a k-dimensional WM(1) process and iff the covariance function of has the triangular property....
Criteria for optimal design of small-sample experiments with correlated observations
We consider observations of a random process (or a random field), which is modeled by a nonlinear regression with a parametrized mean (or trend) and a parametrized covariance function. Optimality criteria for parameter estimation are to be based here on the mean square errors (MSE) of estimators. We mention briefly expressions obtained for very small samples via probability densities of estimators. Then we show that an approximation of MSE via Fisher information matrix is possible, even for small...
Critical values of Fisher's and Siegel's test
Crossings of second-order response processes subjected to LMA loadings.