Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Li, Zeda"'
Autor:
Li, Zeda, Dong, Yuexiao
This article introduces a novel and computationally fast model to study the association between covariates and power spectra of replicated time series. A random covariate-dependent Cram\'{e}r spectral representation and a semiparametric log-spectral
Externí odkaz:
http://arxiv.org/abs/2407.01763
This article introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dep
Externí odkaz:
http://arxiv.org/abs/2109.14677
This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the sp
Externí odkaz:
http://arxiv.org/abs/2102.02794
This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representati
Externí odkaz:
http://arxiv.org/abs/1910.12126
Dissertation/ Thesis
Autor:
Li, Zeda
This dissertation tackles two important problems in modern multivariate nonstationary time series analysis: spectrum analysis and dimension reduction. The first part of the dissertation introduces a nonparametric approach to multivariate time-varying
Externí odkaz:
http://hdl.handle.net/20.500.12613/3196
Autor:
Li, Zeda1 (AUTHOR) zeda.li@baruch.cuny.edu, Wei, William W. S.2 (AUTHOR)
Publikováno v:
Journal of Forecasting. Aug2024, Vol. 43 Issue 5, p1308-1320. 13p.
Publikováno v:
Biometrics. Sep2023, Vol. 79 Issue 3, p1826-1839. 14p.
Autor:
Li, Zeda, Krafty, Robert T.
This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may r
Externí odkaz:
http://arxiv.org/abs/1706.05661
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Akademický článek
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