Results 271 to 280 of about 27,406 (291)

Bias Adjustment Minimizing the Asymptotic Mean Square Error

Communications in Statistics - Theory and Methods, 2013
A method of bias adjustment which minimizes the asymptotic mean square error is presented for an estimator typically given by maximum likelihood. Generally, this adjustment includes unknown population values. However, in some examples, the adjustment can be done without population values.
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PtNLMS algorithm obtained by minimization of mean square error modeled by exponential functions

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010
Using the proportionate-type steepest descent algorithm we represent the current weight deviations in terms of initial weight deviations. Then we attempt to minimize the mean square output error with respect to the gains at a given instant. The corresponding optimal average gains are found using a water-filling procedure.
Kevin T. Wagner, Milos I. Doroslovacki
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A New Estimator of the Variance Based on Minimizing Mean Squared Error

The American Statistician, 2012
In 2005, Yatracos constructed the estimator S 2 2 = c 2 S 2, c 2 = (n + 2)(n − 1)[n(n + 1)]− 1, of the variance, which has smaller mean squared error (MSE) than the unbiased estimator S 2. In this work, the estimator S 2 1 = c 1 S 2, c 1 = n(n − 1)[n(n − 1) + 2]− 1, is constructed and is shown to have the following properties: (a) it has smaller MSE ...
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Linear model averaging by minimizing mean-squared forecast error unbiased estimator

Model Assisted Statistics and Applications, 2016
This paper presents a new ordinary least squares model averaging method which is proposed to be a preferable alternative to Mallows Model Averaging (MMA), Bayesian Model Averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models and selects forecast weights by minimizing the unbiased estimator of ...
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A new histogram-based estimation technique of entropy and mutual information using mean squared error minimization

Computers & Electrical Engineering, 2013
Mutual Information (MI) has extensively been used as a measure of similarity or dependence between random variables (or parameters) in different signal and image processing applications. However, MI estimation techniques are known to exhibit a large bias, a high Mean Squared Error (MSE), and can computationally be very costly.
Abdenour Hacine-Gharbi, Mohamed A. Deriche, Philippe Ravier, Rachid Harba, Tayeb Mohamadi   +4 more
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Minimizing average mean squared error: a comparison of preliminary test estimation to other procedures

Journal of Statistical Computation and Simulation, 1978
The approach of preliminary test estimation has been suggested by Ellerton and Myers (1977) as a means for controlling the size of the J-criterion of the estimator ŷ of the true response,ηIn this paper, the j-criterion associated with the response estimator employed by Ellerton and Myers is evaluated and comparisons made with the j-criteria of several ...
Thomas B. Vassar, Walter H. Carter
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Linear System Approximation By Mean Square Error Minimization In The Time Domain.

1957
PhD ; Electrical engineering ; University of Michigan, Horace H. Rackham School of Graduate Studies ; http://deepblue.lib.umich.edu/bitstream/2027.42/181849/2/5800920 ...
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Choosing the number of factors in partial least squares regression: estimating and minimizing the mean squared error� of prediction

Journal of Chemometrics, 2000
We investigate a number of approaches to estimating the mean squared error of prediction (MSEP) in partial least squares (PLS) regression without resorting to external validation. Using two simulation examples based on real data, performances of the methods are evaluated in terms of their accuracy and their usefulness in determining the optimal number ...
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Determination the Smoothing Constant that Minimizes Mean Absolute Error and Mean Square Deviation

Proceedings of the International Conference on Industrial Engineering and Operations Management, 2021
Abdul Talib Bon, Sukono Sukono, Mustafa Mamat, Agung Prabowo, Agustini Tripena, Danang Adi Pratama, Ibnu Ginanjar Susilo, Zulfatul Mukarromah   +7 more
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A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity

Communications in Statistics - Simulation and Computation, 2022
Catalina García-García, Román Salmerón-Gómez, José García Pérez   +2 more
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