TY - JOUR
T1 - Analysis of variance for high-dimensional time series
AU - Nagahata, Hideaki
AU - Taniguchi, Masanobu
N1 - Funding Information:
Acknowledgements This research was supported by JSPS KAKENHI, Grant Numbers 15H02061 and 26540015. Hideaki Nagahata is also grateful to Hayafumi Watanabe for his helpful comments on real data analysis of this paper.
Publisher Copyright:
© 2018, Springer Nature B.V.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - Analysis of variance (ANOVA) is tailored for independent observations. Recently, there has been considerable demand for ANOVA of high-dimensional and dependent observations in many fields. For example, it is important to analyze differences among industry averages of financial data. However, ANOVA for these types of observations has been inadequately developed. In this paper, we thus present a study of ANOVA for high-dimensional and dependent observations. Specifically, we present the asymptotics of classical test statistics proposed for independent observations and provide a sufficient condition for them to be asymptotically normal. Numerical examples for simulated and radioactive data are presented as applications of these results.
AB - Analysis of variance (ANOVA) is tailored for independent observations. Recently, there has been considerable demand for ANOVA of high-dimensional and dependent observations in many fields. For example, it is important to analyze differences among industry averages of financial data. However, ANOVA for these types of observations has been inadequately developed. In this paper, we thus present a study of ANOVA for high-dimensional and dependent observations. Specifically, we present the asymptotics of classical test statistics proposed for independent observations and provide a sufficient condition for them to be asymptotically normal. Numerical examples for simulated and radioactive data are presented as applications of these results.
KW - Analysis of variance
KW - DCC-GARCH model
KW - High-dimensional dependent disturbance
KW - Non-Gaussian vector stationary process
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U2 - 10.1007/s11203-018-9187-7
DO - 10.1007/s11203-018-9187-7
M3 - Article
AN - SCOPUS:85049597566
SN - 1387-0874
VL - 21
SP - 455
EP - 468
JO - Statistical Inference for Stochastic Processes
JF - Statistical Inference for Stochastic Processes
IS - 2
ER -