= Spike-and-slab regression
{wiki=Spike-and-slab_regression}
Spike-and-slab regression is a statistical technique used in Bayesian regression analysis that aims to perform variable selection while simultaneously estimating regression coefficients. It is particularly useful when dealing with high-dimensional data where the number of predictors may exceed the number of observations, leading to issues such as overfitting. \#\#\# Key Concepts: 1. **Spike-and-Slab Priors**: The technique employs a specific type of prior distribution known as a spike-and-slab prior.
Back to article page