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Title: Testing for trends in high-dimensional time series Authors:  Wei Biao Wu - University of Chicago (United States) [presenting]
Abstract: Statistical inference for trends of high-dimensional time series is considered. Based on a modified L2-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.