Wepropose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box-Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the non-linear GARCH framework. Our simulation studies indicate that t...

It is well known that the generation of random vectors with nonindependent components is difficult. Nevertheless, we propose a new and very simple generation algorithm for multivariate linear densities over point-symmetric domains. Among other applications it can be used to design a simple decompositionrejection algorithm for multivariate concave distributions.

The goal of this work is to investigate the relation of the no-response approach to some other non-iterative reconstruction schemes. We will derive several equivalence statements and dependency results. For simplicity we consider the obstacle reconstruction problem from far field data. In particular, we investigate two versions of the no-response test (NRT) for the inverse scattering problem. T...

The objective of the Power System Stabilizer (PSS) is added to excitation system to enhance the damping during low frequency oscillations. This paper presents a comparison study of Simulated Annealing and fast output sampling based power system stabilizer (PSS) for stability enhancement. Conventional Power system stabilizers (CPSSs) are designed with the phase compensation technique in the freq...

Hashing, which involves learning binary codes to embed high-dimensional data into a similarity-preserving low-dimensional Hamming space, is often formulated as linear dimensionality reduction followed by binary quantization. Linear dimensionality reduction, based on maximum variance formulation, requires leading eigenvectors of data covariance or graph Laplacian matrix. Computing leading singul...

For reconstructing the signal from sampling data, the method based on Shannon’s Sampling theorem is usually employed. In this method, the reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multi layer perceptron as FIR filter. The perceptron which has the weights obtained by learni...

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is qui...