Asymptotic Behavior of $M$ Estimators of $p$ Regression Parameters when $p^2 / n$ is Large; II. Normal Approximation

Abstract

In a general linear model, $Y = X\beta + R$ with $Y$ and $R n$-dimensional, $X$ a $n \times p$ matrix, and $\beta p$-dimensional, let $\hat\beta$ be an $M$ estimator of $\beta$ satisfying $0 = \sum x_i\psi(y_i - x'_i\beta)$. Let $p \rightarrow \infty$ such that $(p \log n)^{3/2} /n \rightarrow 0$. Then $\max_i|x'_i(\hat{\beta} - \beta)| \rightarrow _P 0$, and it is possible to find a uniform normal approximation for the distribution of $\hat{\beta}$ under which arbitrary linear combinations $a'_n (\hat{\beta} - \beta)$ are asymptotically normal (when appropriately normalized) and $(\hat{\beta} - \beta)'(X'X)(\hat{\beta} - \beta)$ is approximately $\chi^2_p$.

Locations

• The Annals of Statistics - View - PDF