One-way MANOVA for functional data via Lawley–Hotelling trace test

Zhu, Tianming; Zhang, Jin‐Ting; Cheng, Ming‐Yen

doi:10.1016/j.jmva.2022.105095

Cited by 6 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work of this study is motivated by the Canadian weather data set, available in the R package fda.usc and discussed in details by [1]. This Canadian weather data set has been studied in the literature of multivariate functional data analysis; see [2] and [3]. It contains the average daily temperature curves and the average daily precipitation curves at 35 Canadian weather stations over a year, obtained via averaging the daily temperature curves and the daily precipitation curves yearly over the period 1960 to 1994.…”

Section: Introductionmentioning

confidence: 99%

A fast and accurate kernel-based independence test with applications to high-dimensional and functional data

Zhang

Zhu

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Testing the dependency between two random variables is an important inference problem in statistics since many statistical procedures rely on the assumption that the two samples are independent. To test whether two samples are independent, a so-called HSIC (Hilbert--Schmidt Independence Criterion)-based test has been proposed. Its null distribution is approximated either by permutation or a Gamma approximation. In this paper, a new HSIC-based test is proposed. Its asymptotic null and alternative distributions are established. It is shown that the proposed test is root-n consistent. A three-cumulant matched chi-squared-approximation is adopted to approximate the null distribution of the test statistic. By choosing a proper reproducing kernel, the proposed test can be applied to many different types of data including multivariate, high-dimensional, and functional data. Three simulation studies and two real data applications show that in terms of level accuracy, power, and computational cost, the proposed test outperforms several existing tests for multivariate, high-dimensional, and functional data.

show abstract

Section: Introductionmentioning

confidence: 99%