Parameter estimation under non-stationary circumstances using extended signal model

Speech Enhancement using Laplacian Mixture Model under Signal Presence Uncertainty

In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE  estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of  noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...

Heteroskedasticity-Autocorrelation Robust Covariance Estimation Under Non-stationary Covariance Processes

The need to estimate variance-covariance matrix in a time series regression arises often in economic applications involving macroeconomic or finance data. In this paper, we study the behavior of two most popular covariance matrix estimators, namely the Kiefer, Vogelsang and Bunzel kernel estimator without truncation (Kiefer, Vogelsang and Bunzel 2000, KVB thereafter) and standard consistent ker...

Signal Parameter Estimation Methods: the Non-eigenvector Based

Parameter Estimation for Periodically Stationary Time Series

The innovations algorithm can be used to obtain parameter estimates for periodically stationary time series models. In this paper we compute the asymptotic distribution for these estimates in the case where the innovations have a finite fourth moment. These asymptotic results are useful to determine which model parameters are significant. In the process, we also develop asymptotics for the Yule...

Effect of Elasticity Parameter on Viscoelastic Fluid in Pipe Flow Using Extended Pom-Pom Model

In this study prediction of the steady-state flow of branched polymer melts in pipe geometry with finite volume method is presented. Our analysis in this study revealed that;for normal-stress tqq , the XPP model can predict this tensor unlike the other viscoelastic models such as PTT or Gieskus which can not predict tqq...