Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/166288
Title: STATISTICAL MODELING FOR HIGH-DIMENSIONAL AND NON-STATIONARY TIME SERIES
Authors: XU XIAOFEI
ORCID iD:   orcid.org/0000-0002-8019-9245
Keywords: Functional time series, serial dependence, high-dimensionality, non-stationarity, integer-valued GARCH model, inhomogeneous volatility
Issue Date: 22-Aug-2019
Citation: XU XIAOFEI (2019-08-22). STATISTICAL MODELING FOR HIGH-DIMENSIONAL AND NON-STATIONARY TIME SERIES. ScholarBank@NUS Repository.
Abstract: In this thesis, we focus on statistical modelling, estimating and forecasting of the dynamic evolution of complex time series with high-dimensionality, special features and non-stationarity using the rich information contained in the large scale dataset. We have proposed and studied three models: the pFAR (the regularised partially functional autoregressive model), the ALG model (adaptive log-linear zero-inflated generalised Poisson autoregressive model with exogenous variable) and the AMS (adaptive multi-stage model), to describe the dynamic behaviour of functional time series with high-dimensional mixed-type covariates, non-stationary integer-valued time series with the features of autocorrelation, heteroscedasticity, over-dispersion and excessive number of zero observations, and time-inhomogeneous volatility process of financial return series respectively. We perform simulation studies to investigate the finite sample performance of the proposed models and demonstrate the applications to real word data.
URI: https://scholarbank.nus.edu.sg/handle/10635/166288
Appears in Collections:Ph.D Theses (Open)

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