Bayesian linear regression with sparse priors
نویسندگان
چکیده
منابع مشابه
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
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where X is the design matrix, ∼ N (0, σI), and β ∼ N (β0, gσ(XTX)−1). The prior on σ is the Jeffreys prior, π(σ) ∝ 1 σ2 , and usually, β0 is taken to be 0 for simplification purposes. The appeal of the method is that there is only one free parameter g for all linear regression. Furthermore, the simplicity of the g-prior model generally leads to easily obtained analytical results. However, we st...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2015
ISSN: 0090-5364
DOI: 10.1214/15-aos1334