High Dimensional Time Series Analysis Tools
c(person("Chen", "Lin", role = c("aut", "cre"),
email = "linchen@smail.swufe.edu.cn"),
person("Guanghui", "Cheng", role = "aut", email = "chenggh845@nenu.edu.cn"),
person("Jinyuan", "Chang", role = "aut", email = "changjinyuan.swufe.edu.cn"),
person("Qiwei ", "Yao", role = "aut", email = "q.yao@lse.ac.uk"))
Chen Lin [aut, cre],
Guanghui Cheng [aut],
Jinyuan Chang [aut],
Qiwei Yao [aut]
Procedures for high-dimensional time series analysis including factor analysis proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2021) preprint, principal component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, unit root test proposed by Chang, Cheng and Yao (2021) <arXiv:2006.07551> and white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>.

New disciminant analysis and data clustering methods for
high dimensional data, based on the asumption that
high-dimensional data live in different subspaces with low
dimensionality proposing a new parametrization of the Gaussian
mixture model which combines the ideas of dimension reduction
and constraints on the model.

High Dimensionnal Supervised Classification and Clustering

New disciminant analysis and data clustering methods for high dimensional data, based on the asumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.

High Dimensional Supervised Classification and Clustering
Laurent Bergé, Charles Bouveyron and Stéphane Girard
Discriminant analysis and data clustering methods for high
dimensional data, based on the assumption that high-dimensional
data live in different subspaces with low dimensionality
proposing a new parametrization of the Gaussian mixture model
which combines the ideas of dimension reduction and constraints
on the model.