x Introduction Literature Review Dissertation Organization and Contribution Abbreviations Array Signal Processing Fundamentals and Current Approaches Problem Formulation Maximum Likelihood DOA Estimation with Gaussian Distributions The Stochastic Maximum Likelihood Method The Deterministic Maximum Likelihood Method The Deterministic Cram er Rao Bound for Gaussian Noise Subspace Based DOA Estima...

Stuetzle and Mittal for ordinary nonparametric kernel regression and Kauermann and Tutz for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators without the need for devices such as second derivative estimation and multiple bandwidths of di erent order We derive a similar estimator in the context of local multivariate es...

We propose techniques for accurate and computationally viable estimation of timing yield using circuit-level Monte Carlo simulation. Our techniques are based on well-known variance reduction approaches from Monte Carlo simulation literature. By adapting these techniques to the yield estimation problem, one can reduce the number of Monte Carlo samples required in order to estimate yield within a...

Over-parameterisation is a key issue in (integrated) environmental modelling. Therefore, a sensitivity analysis (SA) can assist in the proper application of a complex environmental model, such as the Soil and Water Assessment Tool (SWAT). A very powerful sensitivity analysis technique that is gaining popularity in environmental modelling is the variance-based Sobol’ method. Still, the high numb...

The quality of selected AR models depends on the true process in the finite sample practice, on the number of observations, on the estimation algorithm, and on the order selection criterion. Samples are considered to be finite if the maximum candidate model order for selection is greater than 10, where denotes the number of observations. Finite sample formulae give empirical approximations for ...

we propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. this method reduces any additive noise and preserves small patterns and edges of images. a pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. the experimental results show that the proposed...

Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative estimation and multiple bandwidths of diierent order. We derive a similar estimator in the context of local...

The gmwm R package for inference on time series models is mainly based on the quantity called wavelet variance which is derived from a wavelet decomposition of a time series. This quantity provides a means to summarize and graphically represent the features of time series in order to identify possible models. Moreover, it is used as a moment condition for model estimation through the generalize...