## The Annals of Statistics

### On the asymptotic expansion of the empirical process of long-memory moving averages

#### Abstract

Let $X_n = \Sigma_{i=1}^{\infty} a_i \varepsilon_{n-i}$, where the $\varepsilon_i$ are iid with mean 0 finite fourth moment and the $a_i$ are regularly varying with index $-\beta$ where $\beta \epsilon (1/2, 1)$ so that ${X_n}$ has long-range dependence. This covers an important class of the fractional ARIMA process. For $r \geq 0$, let $Y_{N, r} = \sum_{n=1}^N \sum_{1\leq j_1 < \dots < j_r} \Pi_{s=1}^r a_{j_s}, Y_{N, 0} = N, \sigma_{N, r}^2 = \Var(Y_{N, r})$ and $F^{(r)} =$ the rth derivative of the distribution function of $X_n$. The $Y_{N, r}$ are uncorrelated and are stochastically decreasing in r. For any positive integer $p < (2\beta - 1)^{-1}$, it is shown under mild regularity conditions that, with probability 1, $$\sum_{n=1}^N I(X_n \leq x) = \sum_{r=0}^p (-1)^r F^{(r)} (x) Y_{N,r} + o(N^{-\lambda} \sigma_{N,p})\\ \text{uniformly for all x \epsilon\Re \forall 0 < \lambda < (\beta - 1/2) \wedge (1/2 - p(\beta - 1/2))}.$$ This generalizes a host of existing results and provides the vehicle for a number of statistical applications.

#### Article information

Source
Ann. Statist., Volume 24, Number 3 (1996), 992-1024.

Dates
First available in Project Euclid: 20 September 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1032526953

Digital Object Identifier
doi:10.1214/aos/1032526953

Mathematical Reviews number (MathSciNet)
MR1401834

Zentralblatt MATH identifier
0862.60026

#### Citation

Ho, Hwai-Chung; Hsing, Tailen. On the asymptotic expansion of the empirical process of long-memory moving averages. Ann. Statist. 24 (1996), no. 3, 992--1024. doi:10.1214/aos/1032526953. https://projecteuclid.org/euclid.aos/1032526953