Blind separation of source signals usually relies either on the nonGaussianity of the signals or on their linear autocorrelations. A third approach was introduced by Matsuoka et al. (1995), who showed that source separation can be performed by using the nonstationarity of the signals, in particular the nonstationarity of their variances. In this paper, we show how to interpret the nonstationari...

This paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times. It is shown that minimization of a Generalized Method of Moments criterion yields inconsistent estimators of the break fractions, but minimization of the Two Stage Least Squares (2SLS) criterion yields consistent estimators of these parameters. We develop a methodology for e...

The paper is concerned with a problem of finding an optimum experimental design for discriminating between two rival multiresponse models. The criterion of optimality we use is based on the sum of squares of deviations between the models, and picks up the design points for which the divergence is maximum. An important part of our criterion is an additional vector of experimental conditions, whi...

In this paper we present a heuristic perceptually-based termination criterion for a stochastic radiosity algorithms. The proposed criterion makes it possible to terminate the iterative radiosity simulation as soon as any further changes of the radiosity solution are predicted not to be noticed by the human observer. We use tone mapping operators and perceptually uniform CIE L u v colour space t...

Abaract--Equation error (or linear regression) models are known to inherently require the a priori choice for specific signal variables to be considered as regressand and/or regressor. This implies that a model set should be--a priori--restricted in some way in order to define an acceptable identification problem. In the case of approximate identification (i.e. the system to be modelled is not ...

Estimation of model parameters is necessary to predict the behavior of a system. Model parameters are estimated using optimization criteria. Most algorithms use historical data to estimate model parameters. The known target values (actual) and the output produced by the model are compared. The differences between the two form the basis to estimate the parameters. In order to compare different m...

The important normalized maximum likelihood (NML) distribution is obtained via a normalization over all sequences of given length. It has two short-comings: the resulting model is usually not a random process, and in many cases, the normalizing integral or sum is hard to compute. In contrast, the recently proposed sequentially normalized maximum likelihood (SNML) models always comprise a random...

Parameter estimation method of Jelinski-Moranda (JM) model based on weighted nonlinear least squares (WNLS) is proposed. The formulae of resolving the parameter WNLS estimation (WNLSE) are derived, and the empirical weight function and heteroscedasticity problem are discussed. The effects of optimization parameter estimation selection based on maximum likelihood estimation (MLE) method, least s...

Inverse modeling has become a standard technique for estimating hydrogeologic parameters. These parameters are usually inferred by minimizing the sum of the squared differences between the observed system state and the one calculated by a mathematical model. The robustness of the least squares criterion, however, has to be questioned because of the tendency of outliers in the measurements to st...

A magic square of order n consists of the numbers 1 to n placed such that the sum of each row, column and principal diagonal equals the magic sum n(n +1)/2. In addition, an odd ordered magic square is associative or self-complementary if diagonally opposite elements have the same sum (n +1)/2. The magic square is said to be regular Greco-Latin if it can be decomposed as a sum of a pair of Latin...