Author: Barak Oshri

This week we discussed Stability Selection, an extremely general finite sample control technique for structure estimation.

• Paper by Nicolai Meinshausen & Peter Buehlmann

The main result we'll drive towards is an error control bound for the expected number of false discoveries.

## Introduction

In general, we have a task modeled by $p$-dimensional vector $\beta$ which is sparse and only $s < p$ components are non-zero.

We want a procedure to find the set of non-zero explanatory variables $S = \{k : \beta_k \neq 0\}$ that are not vanishing coefficients $N = \{k : \beta_k = 0 \}$. The goal of structure estimation is to infer the set $S$.

### Structure estimation: Regression

In regression, this could be selecting variables from a coefficient vector $\beta$ in a linear model

$$Y = X\beta + \epsilon$$

where $Y$ are observations, $X$ is an $n \times p$ design matrix, and $\epsilon$ is random iid noise.

There is already a suite of well-studied methods to solve the variable selection problem above (for example, the lasso). The aim of stability selection is to enhance and improve existing methods. It is not a new variable selection technique.

### Stability Paths

Generically, we have a set of tuning parameters $\lambda \in \Lambda \subseteq \mathbb{R}^+$ in our task. For every $\lambda$, we get a structure estimate $\hat S^\lambda \subseteq \{1, ..., p\}$.