For testing a linear hypothesis in a censored regression (or censored “Tobit”) model, three test criteria and four test statistics based on least absolute deviations estimates of parameters are proposed and their limiting chi-square distributions are established. Some consistent estimates of nuisance parameters are obtained for use in computing the test statistics. A simulation study for small ...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

This paper briefly discussed the principle of the linear constraints minimum variance (LCMV) algorithm and the least mean square (LMS) algorithm on the basis of the construction of an uniform circular antenna array. Then it compared the parameters of the two, such as signal-to-noise ratio, bit error rate, the average error, convergence rate, and so on, and then it was simulated by MATLAB. The s...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

This paper presents an adaptive digital resolution improvement method for extrapolating and recursive analog-to-digital converters (ADCs). The presented adaptively enhanced ADC (AE-ADC) digitally estimates the digital equivalent of the input signal by utilizing an adaptive digital filter (ADF). The least mean squares (LMS) algorithm also determines the coefficients of the ADF block. In this sch...

This paper deals with the problem of Adaptive Noise Cancellation (ANC) when only a corrupted speech signal with an additive Gaussian white noise is available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and represented in the state-space domain. All the approaches based on the Kalma...

This paper deals with the problem of speech enhancement when only a corrupted speech signal is available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and represented in the state-space domain. Various approaches based on the Kalman filter are presented in the literature. They usuall...