and Applied Analysis 3 with the initial conditions

We consider cross-sectional data that exhibit no spatial correlation, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of …nancial econometrics, entailing a form of spatial autoregression, can explain such behaviour. The parameters are estimated by pseudo Gaussian maximum likelihood based on log-transformed squares, and consisten...

this paper presents a method for identification of linear system physical parameters (structural mass, damping and stiffness matrices) using the inverse solution of equation of motion in the frequency domain, by focus on the reducing the illconditioning effect. the method utilizes the measured responses from the forced vibration test of structure in order to identify the system properties and d...

recently, the block-pulse functions (bpfs) are used in solving electromagnetic scattering problem, which are modeled as linear fredholm integral equations (fies) of the second kind. but the theoretical aspect of this method has not fully investigated yet. in this article, in addition to presenting a new approach for solving fie of the second kind, the theory of both methods is investigated as a...

In this paper, a wavelet based method is proposed to identify the constant coefficients of a second order linear system and is compared with the least squares method. The proposed method shows improved accuracy of parameter estimation as compared to the least squares method. Additionally, it has the advantage of smaller data requirement and storage requirement as compared to the least squares m...

Obstacle recognition is one of the key technologies for Unmanned Undersea Vehicle (UUV). In this paper, the design of obstacle recognition based on multi-beam forward looking sonar is presented. First of all, Gaussian mixed model operator is adopted in adaptive threshold segmentation to segment the sonar image into regions of interest and background. Secondly, we use morphological operation to ...

In computer vision tasks, it frequently happens that gross noise and pseudo outliers occupy the absolute majority of the data. During the past several decades, a lot of robust estimators were developed to find parameters of a model from heavily contaminated data. However, correctly estimating the parameters of a model is not enough to differentiate inliers from outliers. Robust scale estimation...

The application of bootstrap methods to regression models helps approximate the distribution of the coefficients and the distribution of the prediction errors. In this paper, we are concerned with the application of the bootstrap techniques to determine prediction intervals on econometric models when the regressors are known. We investigate problems associated with its application: determinatio...