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Activity Number: 477 - Complex Time Series Analysis
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #300083
Title: Testing for Trends in High-Dimensional Time Series
Author(s): Likai Chen* and Wei Biao Wu
Companies: Washington University in Saint Louis and University of Chicago
Keywords: Asymptotic normality; Gaussian multiplier testing procedure; Temporal and spatial dependence; Parametric testing

We consider statistical inference for trends of high-dimensional time series. Based on a modified ?2 distance between parametric and nonparametric trend estimators, we propose a de-diagonalized quadratic form test statistic for testing patterns on trends, such as linear, quadratic, or parallel forms. We develop an asymptotic theory for the test statistic. A Gaussian multiplier testing procedure is proposed and it has an improved finite sample performance. Our testing procedure is applied to a spatial temporal temperature data gathered from various locations across America. A simulation study is also presented to illustrate the performance of our testing method.

Authors who are presenting talks have a * after their name.

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