Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalize...

In this paper, we present a neutron spectrum unfolding technique based on a modification of the least-squares method. The main innovation is the use of a Krylov subspace iteration method to solve the least-squares normal equations. This method was employed because it performs better on ill-conditioned systems of linear equations as compared with standard direct-solution methods. Three different...

in this paper, the pricing of a european call option on the underlying asset is performed by using a monte carlo method, one of the powerful simulation methods, where the price development of the asset is simulated and value of the claim is computed in terms of an expected value. the proposed approach, applied in monte carlo simulation, is based on the black-scholes equation which generally def...

In this paper we present two robust estimates for GARCH(p,q) models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estimated conditional variances. We study the asymptotic properties of our estimates proving consisten...

We propose a simple modification to the general-to-specific lag-length selection method typically employed in a standard augmented Dickey-Fuller (ADF) test and apply it to examine the stationarity of OECD countries’ inflation rates. Instead of using the entire set of lags selected by the general-to-specific method, we suggest using only the lags that are statistically significant. Our Monte Car...

Aggressive device scaling has made it imperative to account for process variations in the design flow. A robust model of process variations is an essential requirement for any meaningful variation aware design analysis and optimization. Unfortunately the previous approaches on extracting spatial correlation function assume ergodicity and isotropy while estimating the inter-die(global) component...

The expectation-maximization algorithm has been advocated recently by a number of authors for fitting generalized linear mixed models. Since the E-step typically involves analytically intractable integrals, one approach is to approximate them by Monte Carlo methods. However, in practice, the Monte Carlo sample sizes required for convergence are often prohibitive. In this paper we show how rando...