1. High-dimensional posterior consistency of the Bayesian lasso.
- Author
-
Dasgupta, Shibasish
- Subjects
- *
BAYESIAN analysis , *MATHEMATICAL variables , *PARAMETER estimation , *FREQUENTIST statistics , *HIGH-dimensional model representation , *ALGORITHMS - Abstract
This paper considers posterior consistency in the context of high-dimensional variable selection using the Bayesian lasso algorithm. In a frequentist setting, consistency is perhaps the most basic property that we expect any reasonable estimator to achieve. However, in a Bayesian setting, consistency is often ignored or taken for granted, especially in more complex hierarchical Bayesian models. In this paper, we have derived sufficient conditions for posterior consistency in the Bayesian lasso model with the orthogonal design, where the number of parameters grows with the sample size. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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