scholarly journals Methods for estimation of covariance matrices and covariance components for the Hanford Waste Vitrification Plant Process

1996 ◽  
Author(s):  
M.F. Bryan ◽  
G.F. Piepel ◽  
D.B. Simpson
Keyword(s):  
Covariance Matrices ◽  
Waste Vitrification ◽  
Covariance Components ◽  
Plant Process

Estimates of the complete genetic covariance matrix for traits in multi-trait genetic evaluation of Australian Hereford cattle

2004 ◽  
Vol 55 (2) ◽  
pp. 195 ◽  
Author(s):  
Karin Meyer ◽  
David J. Johnston ◽  
Hans-Ulrich Graser
Keyword(s):  
Genetic Parameters ◽  
Carcass Traits ◽  
Early Growth ◽  
Genetic Evaluation ◽  
Covariance Matrices ◽  
Ultrasound Scanning ◽  
Genetic Covariance ◽  
Individual Traits ◽  
Hereford Cattle ◽  
Covariance Components

Estimates of covariance components among all 22 traits considered in the current multi-trait genetic evaluation of Australian Hereford cattle were obtained. Traits included 5 weight traits, 8 traits measured through live ultrasound scanning, 3 traits related to reproductive performance, and 6 carcass traits. Estimates were obtained by restricted maximum likelihood, carrying out a series of bivariate analyses. Data for each analysis were selected attempting to maximise the number of animals or animal–parent pairs that had both traits recorded. Estimates were pooled using a weighted 'iterative summing of expanded part matrices' procedure, which ensured positive semi-definite covariance matrices. Models of analyses for individual traits closely resembled those used in genetic evaluation. Results generally agreed with literature results, although estimates of genetic parameters for carcass traits that had few records available tended to fluctuate. Except for 'days to calving', heritability estimates were moderate to high for all traits. Genetic parameters for early growth were different to those for other breeds, with maternal effects for weaning weight being considerably more important and the heritability somewhat lower.

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

Systematic error of LSM-estimation of covariance components of biperiodically correlated random signals

2019 ◽  
Vol 2019 (47) ◽  
pp. 26-33
Author(s):  
I. M. Javorskyj ◽  
◽  
O. Y. Dzeryn ◽  
R. M. Yuzefovych ◽  
◽  
...  
Keyword(s):  
Systematic Error ◽  
Covariance Components ◽  
Random Signals

Pemberian Umpan Balik Dalam Meningkatkan Hasil Belajar dan Minat Belajar Mahasiswa

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Seruni Seruni ◽  
Nurul Hikmah
Keyword(s):  
Information Technology ◽  
Learning Outcomes ◽  
Test Data ◽  
Random Sampling ◽  
Covariance Matrices ◽  
Test Results ◽  
Immediate Feedback ◽  
Statistics Course ◽  
Independent Variable ◽  
Descriptive Statistical Analysis

<p>The purpose of this study is to find and analyze the effect of feedback on <br />learning outcomes in mathematics and an interest in basic statistics course. The <br />population in this study are affordable Information Technology Student cademic Year 2012/2013 Semester II Indraprasta PGRI University of South Jakarta. Sample The study sample was obtained through random sampling. This study used an experimental method to the analysis using the MANOVA test. This study has three variables, consisting of: one independent variable, namely the provision of feedback (immediate and delayed), and two dependent variable is the result of interest in the study of mathematics and basic statistics course. The data was collected for the test results to learn mathematics, and a questionnaire for the interest in basic statistics course. Collected data were analyzed using the MANOVA test. Before the data were analyzed, first performed descriptive statistical analysis and test data analysis requirements (test data normality and homogeneity of covariance matrices). The results show that the learning outcomes of interest in mathematics and basic statistics course for students who are given immediate feedback higher than students given feedback delayed. <br /><br /></p>

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