scholarly journals AN APPROXIMATION OF THE MINIMUM-VARIANCE ESTIMATOR OF HERITABILITY BASED ON VARIANCE COMPONENT ANALYSIS

Genetics ◽  
1981 ◽  
Vol 98 (2) ◽  
pp. 417-426
Author(s):  
M Grossman ◽  
H W Norton
Keyword(s):  
Minimum Variance ◽  
Variance Component Analysis ◽  
Weighted Estimate ◽  
Sampling Variance ◽  
Variance Estimator ◽  
Hierarchical Analysis ◽  
Components Of Variance ◽  
Minimum Variance Estimator ◽  
Variance Estimate ◽  
Desirable Behavior

ABSTRACT An approximate minimum-variance estimate of heritability (h  2) is proposed, using the sire and dam components of variance from a hierarchical analysis of variance. The minimum sampling variance is derived for unbalanced data. Optimum structures for the estimation of h  2 are given for the balanced case. The degree to which ĥ  2 is more precise than the equally weighted estimate ĥ  2  S+D is a function of the size and structure of the sample used. However, computer simulation reveals that ĥ  2 has less desirable behavior than ĥ  2  S+D. An iterative procedure improved the estimation of h  2, especially in small populations, when those values of ĥ  2  S or ĥ  2  D outside the range of the parameter were constrained to zero or unity.

Fast minimum variance estimator for limited angle CT image reconstruction

1981 ◽  
Vol 8 (5) ◽  
pp. 695-702 ◽  
Author(s):  
Michael H. Buonocore ◽  
William R. Brody ◽  
Albert Macovski
Keyword(s):  
Image Reconstruction ◽  
Minimum Variance ◽  
Ct Image ◽  
Variance Estimator ◽  
Minimum Variance Estimator ◽  
Ct Image Reconstruction ◽  
Limited Angle

scholarly journals A distributed minimum variance estimator for sensor networks

2008 ◽  
Vol 26 (4) ◽  
pp. 609-621 ◽  
Author(s):  
A. Speranzon ◽  
C. Fischione ◽  
K. Johansson ◽  
A. Sangiovanni-Vincentelli
Keyword(s):  
Sensor Networks ◽  
Minimum Variance ◽  
Variance Estimator ◽  
Minimum Variance Estimator

scholarly journals Error Propagation of Capon’s Minimum Variance Estimator

2021 ◽  
Vol 9 ◽  
Author(s):  
S. Toepfer ◽  
Y. Narita ◽  
D. Heyner ◽  
U. Motschmann
Keyword(s):  
Measurement Errors ◽  
Error Propagation ◽  
Minimum Variance ◽  
Least Square ◽  
Variance Estimator ◽  
Position Errors ◽  
Minimum Variance Estimator ◽  
Least Square Fit ◽  
Special Case ◽  
Key Parameter

The error propagation of Capon’s minimum variance estimator resulting from measurement errors and position errors is derived within a linear approximation. It turns out, that Capon’s estimator provides the same error propagation as the conventionally used least square fit method. The shape matrix which describes the location depence of the measurement positions is the key parameter for the error propagation, since the condition number of the shape matrix determines how the errors are amplified. Furthermore, the error resulting from a finite number of data samples is derived by regarding Capon’s estimator as a special case of the maximum likelihood estimator.

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