Approximations for Markov decision problems
Approximations of believability functions under incomplete identification of sets of compatible states
Approximations of the distribution of the sum of random variables from the generalized logistic distribution
Approximative solutions of stochastic optimization problems
The aim of this paper is to present some ideas how to relax the notion of the optimal solution of the stochastic optimization problem. In the deterministic case, -minimal solutions and level-minimal solutions are considered as desired relaxations. We call them approximative solutions and we introduce some possibilities how to combine them with randomness. Relations among random versions of approximative solutions and their consistency are presented in this paper. No measurability is assumed, therefore,...
AR models with uniformly distributed noise
AR models are frequently used but usually with normally distributed white noise. In this paper AR model with uniformly distributed white noise are introduces. The maximum likelihood estimation of unknown parameters is treated, iterative method for the calculation of estimates is presented. A numerical example of this procedure and simulation results are also given.
AR(1) processes with given moments of marginal distribution
Are Robust Estimation Methods Useful in the Structural Errors-in-Variables Model?.
Are volatility indices in international stock markets forward looking?
Arithmetizing continuous distributions: numerical aspects.
ARMA models with nonstationary white noise
Artificial neural networks in time series forecasting: a comparative analysis
Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series...
Aspects of analysis of multivariate failure time data.
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Aspects of control for the normal Markov processes.
Assessing influence in survival data with a cured fraction and covariates.
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to...
Association entre variables qualitatives ordinales «nettes» ou «floues»
Asymmetric recursive methods for time series
The problem of asymmetry appears in various aspects of time series modelling. Typical examples are asymmetric time series, asymmetric error distributions and asymmetric loss functions in estimating and predicting. The paper deals with asymmetric modifications of some recursive time series methods including Kalman filtering, exponential smoothing and recursive treatment of Box-Jenkins models.
Asymmetric semilinear copulas
We complement the recently introduced classes of lower and upper semilinear copulas by two new classes, called vertical and horizontal semilinear copulas, and characterize the corresponding class of diagonals. The new copulas are in essence asymmetric, with maximum asymmetry given by . The only symmetric members turn out to be also lower and upper semilinear copulas, namely convex sums of and .
Asymptotic analysis for bifurcating autoregressive processes via a martingale approach.
Asymptotic analysis of minimum volume confidence regions for location-scale families
An asymptotic analysis, when the sample size n tends to infinity, of the optimal confidence region established in Czarnowska and Nagaev (2001) is considered. As a result, two confidence regions, both close to the optimal one when n is sufficiently large, are suggested with a mild assumption on the distribution of a location-scale family.
Asymptotic approximations to the Bayes posterior risk.