Bayesian analysis for variance component estimation of a hierarchical model: Effect of thinning intervals

2018 ◽  
Author(s):  
Doğan Narinç ◽  
Ali Aygün
Keyword(s):  
Bayesian Analysis ◽  
Hierarchical Model ◽  
Variance Component ◽  
Variance Component Estimation ◽  
Component Estimation

GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

2021 ◽  
pp. 1-16
Author(s):  
Hong Hu ◽  
Xuefeng Xie ◽  
Jingxiang Gao ◽  
Shuanggen Jin ◽  
Peng Jiang
Keyword(s):  
Stochastic Model ◽  
Least Squares ◽  
Stochastic Models ◽  
Variance Component ◽  
Satellite System ◽  
Variance Component Estimation ◽  
Short Baseline ◽  
Component Estimation ◽  
Zero Baseline ◽  
Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

scholarly journals An overview of variance component estimation

Metrika ◽  
1995 ◽  
Vol 42 (1) ◽  
pp. 215-230 ◽  
Author(s):  
Shayle R. Searle
Keyword(s):  
Variance Component ◽  
Variance Component Estimation ◽  
Component Estimation

scholarly journals Relationships between longevity and conformation traits in Czech Fleckvieh cows

2009 ◽  
Vol 54 (No. 9) ◽  
pp. 385-394 ◽  
Author(s):  
L. Zavadilová ◽  
E. Němcová ◽  
M. Štípková ◽  
J. Bouška
Keyword(s):  
Variance Component ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Variance Component Estimation ◽  
Body Measurements ◽  
Linear Type ◽  
Zero Correlation ◽  
Type Traits ◽  
Component Estimation

The relationships between conformation and longevity traits were analysed in 58 493 Czech Fleckvieh cows first calved from 1994 to 2003. All cows were scored for conformation during the first lactation. Genetic correlations between longevity and conformation traits were estimated by bivariate runs using the VCE 4.0 program for variance component estimation. The values of heritability for conformation traits were in the range from 0.06 to 0.63 and for longevity traits from 0.04 to 0.05. Low or intermediate genetic relationships between recorded linear traits and longevity trait were found. The correlations were lower for functional longevity. Body measurements showed negative genetic correlations with real as well as functional longevity (–0.12 to –0.29). The dairy character negatively correlated with longevity traits (–0.18 to –0.26). The muscularity and udder showed a zero correlation with functional longevity, while the feet and legs were not correlated with real longevity. The highest positive genetic correlations between real longevity and objectively scored linear type traits were found for hock (0.24), rear udder attachment (0.28), fore udder length (0.16) and central ligament (0.11). On the contrary, the correlation between the udder depth and the milk-corrected longevity was positive (0.28) and higher than in the case of real longevity.

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