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Microphone arrays provide new opportunities for noise reduction and speech enhancement. This paper presents anovel decomposition of the estimation problems for short-time spectral amplitude (STSA), lo...
We propose a noise cancellation technique that performs robustly in the presence of poor channel estimates and channel synchronization errors. The technique is based on the assumption that the signals...
Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in speech enhancement.One state-of-the-art approach is the minima controlledrecursive averaging (MCRA). Thi...
We introduce a novel method for sparse regression and variable selection, which is inspired by modern ideas in multiple testing. Imagine we have observations from the linear model y = Xβ + z, then we ...
We consider the parametric estimation of the driving L\'evy process of a multivariate continuous-time autoregressive moving average (MCARMA) process, which is observed on the discrete time grid $(0,h,...
One of most important problems concerning the ARFIMA time series model is the estimation of fractional parameter d. Various methods have been used to solve this problem, such as the log-periodogram r...
This paper deal with estimation of p(x > y) when x and y are tow independent pareto distribution . the maximum likelihood and its asymptotical distribution is obtain.
The estimation of the disease incidents was previously analyzed using a classical approach. However, this approach features large outlying relative risks and considered as misleading due to several ma...
Bayes and classical estimators have been obtained for two parameters aexponentiated Pareto distribution when sample is available from complete, type I and type II censoring scheme. Bayes estimators ha...
Gupta and Kundu (1999) defined the cumulative distribution function of the generalized exponential (GE). It has many properties that are quite similar to those of the gamma distribution. This paper de...
The Pearson product-moment correlation commonly used as statistical dependence measure was developed assuming normal marginal and addresses only linear dependence. In most applications, the distributi...
Operational equations can be used for the estimation of data missing from prismatic arrays. This paper continues the discussion of that subject. New interpolating equations for data in prismatic and r...
Mutual Information (MI) measures the degree of association between variables in nonlinear model as well as linear models. It can also be used to measure the dependency between variables in mixture dis...
We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot...
Recently, Sarhan and Zaindin (2008) introduced a generalization of the Weibull distribution and named it as modified Weibull distribution. In this paper, we deal with the problem of estimating the par...

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