Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise

2016 ◽  
Vol 101 ◽  
pp. 250-276 ◽  
Author(s):  
Namgil Lee ◽  
Hyemi Choi ◽  
Sung-Ho Kim
Keyword(s):  
High Dimensional ◽  
Shrinkage Estimation ◽  
Var Models ◽  
Scale Mixture ◽  
Normal Distributions ◽  
Mixture Of Normal Distributions

Testing homogeneity in a scale mixture of normal distributions

2015 ◽  
Vol 57 (2) ◽  
pp. 499-516 ◽  
Author(s):  
Xiaoqing Niu ◽  
Pengfei Li ◽  
Peng Zhang
Keyword(s):  
Scale Mixture ◽  
Normal Distributions ◽  
Testing Homogeneity ◽  
Mixture Of Normal Distributions

scholarly journals Normal approximation for mixtures of normal distributions and the evolution of phenotypic traits

2021 ◽  
Vol 53 (1) ◽  
pp. 162-188
Author(s):  
Krzysztof Bartoszek ◽  
Torkel Erhardsson
Keyword(s):  
Normal Distribution ◽  
Phenotypic Trait ◽  
Phenotypic Traits ◽  
Normal Distributions ◽  
Average Value ◽  
Conditional Moments ◽  
Mixture Of Normal Distributions ◽  
Explicit Bounds ◽  
Mixtures Of Normal Distributions ◽  
Parameter Values

AbstractExplicit bounds are given for the Kolmogorov and Wasserstein distances between a mixture of normal distributions, by which we mean that the conditional distribution given some $\sigma$ -algebra is normal, and a normal distribution with properly chosen parameter values. The bounds depend only on the first two moments of the first two conditional moments given the $\sigma$ -algebra. The proof is based on Stein’s method. As an application, we consider the Yule–Ornstein–Uhlenbeck model, used in the field of phylogenetic comparative methods. We obtain bounds for both distances between the distribution of the average value of a phenotypic trait over n related species, and a normal distribution. The bounds imply and extend earlier limit theorems by Bartoszek and Sagitov.

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