Dr Yang Yang
Lecturer in Data Science and Innovation
School of Information and Physical Sciences (Data Science and Statistics)
- Email:yang.yang10@newcastle.edu.au
- Phone: (02) 4921 8622
Career Summary
Biography
Dr Yang Yang received his PhD degree in Statistics from the Australian National University (ANU) in 2020. Before PhD, he obtained bachelor's degrees in Actuarial Studies (First Class Honours) and Commerce from ANU in 2015. Prior to joining the University of Newcastle, he worked as a Research Fellow in the Department of Econometrics and Business Statistics at Monash Business School during 2020-2022. His current research interests focus on functional data analysis, time series analysis, demographic forecasting, climate data modelling, and functional data tools for health data.
Qualifications
- DOCTOR OF PHILOSOPHY, Australian National University
- BACHLOR OF ACTUARIAL STUDIES, Australian National University
Keywords
- Climate Data Analysis
- Demography Forecasting
- Functional Data Analysis
- Mortality Modelling
- Panel Data Modelling
- Time Series Modelling
Languages
- English (Fluent)
- Mandarin (Mother)
Fields of Research
Code | Description | Percentage |
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380202 | Econometric and statistical methods | 30 |
490501 | Applied statistics | 40 |
490511 | Time series and spatial modelling | 30 |
Professional Experience
UON Appointment
Title | Organisation / Department |
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Lecturer in Data Science and Innovation | University of Newcastle School of Information and Physical Sciences Australia |
Academic appointment
Dates | Title | Organisation / Department |
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3/8/2020 - 1/8/2022 | Research Fellow | Monash University Faculty of Business & Economics Australia |
Awards
Scholarship
Year | Award |
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2015 |
ANU RSFAS Honours Scholarship Australian National University |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Journal article (7 outputs)
Year | Citation | Altmetrics | Link | |||||
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2024 |
Yang Y, Shang HL, Raymer J, 'Forecasting Australian fertility by age, region, and birthplace', International Journal of Forecasting, 40 532-548 (2024) [C1]
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2022 |
Yang Y, Yang Y, Shang HL, 'Feature extraction for functional time series: Theory and application to NIR spectroscopy data', Journal of Multivariate Analysis, 189 (2022) [C1] We propose a novel method to extract global and local features of functional time series. The global features concerning the dominant modes of variation over the entire function d... [more] We propose a novel method to extract global and local features of functional time series. The global features concerning the dominant modes of variation over the entire function domain, and local features of function variations over particular short intervals within function domain, are both important in functional data analysis. Functional principal component analysis (FPCA), though a key feature extraction tool, only focus on capturing the dominant global features, neglecting highly localized features. We introduce a FPCA-BTW method that initially extracts global features of functional data via FPCA, and then extracts local features by block thresholding of wavelet (BTW) coefficients. Using Monte Carlo simulations, along with an empirical application on near-infrared spectroscopy data of wood panels, we illustrate that the proposed method outperforms competing methods including FPCA and sparse FPCA in the estimation functional processes. Moreover, extracted local features inheriting serial dependence of the original functional time series contribute to more accurate forecasts. Finally, we develop asymptotic properties of FPCA-BTW estimators, discovering the interaction between convergence rates of global and local features.
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2022 |
Yang Y, Shang HL, Cohen JE, 'Temporal and spatial Taylor's law: Application to Japanese subnational mortality rates', Journal of the Royal Statistical Society. Series A: Statistics in Society, 185 1979-2006 (2022) [C1] Taylor's law is a widely observed empirical pattern that relates the variances to the means of population densities. We present four extensions of the classical Taylor's... [more] Taylor's law is a widely observed empirical pattern that relates the variances to the means of population densities. We present four extensions of the classical Taylor's law (TL): (1) a cubic extension of the linear TL describes the mean¿variance relationship of human mortality at subnational levels well; (2) in a time series, long-run variance measures not only variance but also autocovariance, and it is a more suitable measure than variance alone to capture temporal/spatial correlation; (3) an extension of the classical equally weighted spatial variance takes account of synchrony and proximity; (4) robust linear regression estimators of TL parameters reduce vulnerability to outliers. Applying the proposed methods to age-specific Japanese subnational death rates from 1975 to 2018, we study temporal and spatial variations, compare different coefficient estimators, and interpret the implications. We apply a clustering algorithm to the estimated TL coefficients and find that cluster memberships are strongly related to prefectural gross domestic product. The time series of spatial TL coefficients has a decreasing trend that confirms the narrowing gap between rural and urban mortality in Japan.
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2022 |
Yang Y, Shang HL, 'Is the Group Structure Important in Grouped Functional Time Series?', Journal of Data Science, 303-324 (2022) [C1]
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2021 |
Shang HL, Yang Y, 'Forecasting Australian subnational age-specific mortality rates', Journal of Population Research, 38 (2021) [C1] When modeling sub-national mortality rates, it is important to incorporate any possible correlation among sub-populations to improve forecast accuracy. Moreover, forecasts at the ... [more] When modeling sub-national mortality rates, it is important to incorporate any possible correlation among sub-populations to improve forecast accuracy. Moreover, forecasts at the sub-national level should aggregate consistently across the forecasts at the national level. In this study, we apply a grouped multivariate functional time series to forecast Australian regional and remote age-specific mortality rates and reconcile forecasts in a group structure using various methods. Our proposed method compares favorably to a grouped univariate functional time series forecasting method by comparing one-step-ahead to five-step-ahead point forecast accuracy. Thus, we demonstrate that joint modeling of sub-populations with similar mortality patterns can improve point forecast accuracy.
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2019 |
Shang HL, Yang Y, Kearney F, 'Intraday forecasts of a volatility index: functional time series methods with dynamic updating', Annals of Operations Research, 282 331-354 (2019) [C1] As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts an... [more] As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts and academics. We consider discrete reported intraday VIX tick values as realisations of a collection of curves observed sequentially on equally spaced and dense grids over time and utilise functional data analysis techniques to produce 1-day-ahead forecasts of these curves. The proposed method facilitates the investigation of dynamic changes in the index over very short time intervals as showcased using the 15-s high-frequency VIX index values. With the help of dynamic updating techniques, our point and interval forecasts are shown to enjoy improved accuracy over conventional time series models.
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2018 |
Shi Y, Yang Y, 'Modeling high frequency data with long memory and structural change: A-HYEGARCH model', Risks, 6 (2018) [C1] In this paper, we propose an Adaptive Hyperbolic EGARCH (A-HYEGARCH) model to estimate the long memory of high frequency time series with potential structural breaks. Based on the... [more] In this paper, we propose an Adaptive Hyperbolic EGARCH (A-HYEGARCH) model to estimate the long memory of high frequency time series with potential structural breaks. Based on the original HYGARCH model, we use the logarithm transformation to ensure the positivity of conditional variance. The structural change is further allowed via a flexible time-dependent intercept in the conditional variance equation. To demonstrate its effectiveness, we perform a range of Monte Carlo studies considering various data generating processes with and without structural changes. Empirical testing of the A-HYEGARCH model is also conducted using high frequency returns of S&P 500, FTSE 100, ASX 200 and Nikkei 225. Our simulation and empirical evidence demonstrate that the proposed A-HYEGARCH model outperforms various competing specifications and can effectively control for structural breaks. Therefore, our model may provide more reliable estimates of long memory and could be a widely useful tool for modelling financial volatility in other contexts.
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Show 4 more journal articles |
Conference (1 outputs)
Year | Citation | Altmetrics | Link | |||||
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2017 |
Shang HL, Yang Y, 'Grouped multivariate functional time series method: An application to mortality forecasting', FUNCTIONAL STATISTICS AND RELATED FIELDS, A Coruna, SPAIN (2017) [E1]
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Dr Yang Yang
Position
Lecturer in Data Science and Innovation
DSS
School of Information and Physical Sciences
College of Engineering, Science and Environment
Focus area
Data Science and Statistics
Contact Details
yang.yang10@newcastle.edu.au | |
Phone | (02) 4921 8622 |
Office
Room | SR116 |
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Building | Social Science Building |
Location | Callaghan University Drive Callaghan, NSW 2308 Australia |