BEHAVIOR OF THE WALD’S TEST FOR A PROPORTION BASED ON A SHRINKAGE ESTIMATOR

2020 ◽  
Vol 63 (2) ◽  
pp. 207-218
Author(s):  
Félix Almendra-Arao ◽  
Hortensia J. Reyes-Cervantes ◽  
Marcos Morales-Cortés
Keyword(s):  
Shrinkage Estimator

Regression tools for CO2inversions: application of a shrinkage estimator to process attribution

Tellus B ◽  
2006 ◽  
Vol 58 (4) ◽  
Author(s):  
Benjamin A. Shaby ◽  
Christopher B. Field
Keyword(s):  
Shrinkage Estimator

Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

2019 ◽  
Vol 10 (1) ◽  
pp. 83-97
Author(s):  
Amoon Khalil ◽  
Mohiedin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Detection Performance ◽  
Shrinkage Estimator ◽  
Detection Window ◽  
Simulation Results

Spectrum Sensing is one of the major steps in Cognitive Radio. There are many methods to conduct Spectrum Sensing. Each method has different detection performances. In this article, the authors propose a modification of one of these methods based on MME algorithm and OAS estimator. In MME&OAS method, in each detection window, OAS estimates the covariance matrix of the signal then the MME algorithm detects the signal on the estimated matrix. In the proposed algorithm, authors assumed that there is correlation between two consecutive decisions, so authors suggest the OAS estimator depending on the last detection decision, and then detect the signal using MME algorithm. Simulation results showed enhancement in detection performance (about 2dB when detection probability is 0.9. compared to MME&OAS method).

scholarly journals On double stage minimax-shrinkage estimator for generalized Rayleigh model

2016 ◽  
Vol 5 (1) ◽  
pp. 39 ◽  
Author(s):  
Abbas Najim Salman ◽  
Maymona Ameen
Keyword(s):  
Sample Size ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Shrinkage Estimator ◽  
Squared Error ◽  
Expected Sample Size ◽  
Generalized Rayleigh Distribution ◽  
The Mean

<p>This paper is concerned with minimax shrinkage estimator using double stage shrinkage technique for lowering the mean squared error, intended for estimate the shape parameter (a) of Generalized Rayleigh distribution in a region (R) around available prior knowledge (a<sub>0</sub>) about the actual value (a) as initial estimate in case when the scale parameter (l) is known .</p><p>In situation where the experimentations are time consuming or very costly, a double stage procedure can be used to reduce the expected sample size needed to obtain the estimator.</p><p>The proposed estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y(<strong>×</strong>) and suitable region R.</p><p>Expressions for Bias, Mean squared error (MSE), Expected sample size [E (n/a, R)], Expected sample size proportion [E(n/a,R)/n], probability for avoiding the second sample and percentage of overall sample saved  for the proposed estimator are derived.</p><p>Numerical results and conclusions for the expressions mentioned above were displayed when the consider estimator are testimator of level of significanceD.</p><p>Comparisons with the minimax estimator and with the most recent studies were made to shown the effectiveness of the proposed estimator.</p>

scholarly journals Preliminary test-shrinkage estimators

1983 ◽  
Vol 2 (2) ◽  
pp. 88-91
Author(s):  
H. H. Lemmer
Keyword(s):  
Preliminary Test ◽  
Shrinkage Estimator ◽  
Shrinkage Estimators ◽  
Poisson Distributions

The advantages of using the very simple shrinkage estimator TL proposed by Lemmer rather than that proposed by Mehta and Srivivasan in the case of preliminary test estimators for parameters of the normal, binomial and Poisson distributions are examined.

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