Ben-Chang Shia
Biographic Data
| ID | 8920173 |
|---|---|
| NAME | Ben-Chang Shia |
| GIVEN NAMES | Ben-Chang |
| FAMILY NAME | Shia |
| SIGNATURE | SHIA B |
| AFFILIATIONS | Taipei Medical University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Bayesian Dynamic Matrix Factor Models
The growth in data volume and the expansion in data dimensionality are challenging the analysis of high-dimensional matrix time series. Factor models for matrix-valued high-dimensional time series are a powerful tool for reducing the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix factor models are nearly all static and struggle to capture the dynamics and time evolution of data. In this paper, …
Prediction of Number of Cases of 2019 Novel Coronavirus (Covid-19) Using Social Media Search Index
Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series …
No prominent works on this page.
Prediction of Number of Cases of 2019 Novel Coronavirus (Covid-19) Using Social Media Search Index
Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series …
Bayesian Dynamic Matrix Factor Models
The growth in data volume and the expansion in data dimensionality are challenging the analysis of high-dimensional matrix time series. Factor models for matrix-valued high-dimensional time series are a powerful tool for reducing the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix factor models are nearly all static and struggle to capture the dynamics and time evolution of data. In this paper, …
Computer Science (2 works) · Mathematics (2 works) · 2019-20 coronavirus outbreak (1 works) · Artificial Intelligence (1 works) · Bayesian probability (1 works) · Betacoronavirus (1 works) · Complex Systems and Time Series Analysis (1 works) · Computational and Text Analysis Methods (1 works) · Coronavirus (1 works) · Coronavirus disease 2019 (COVID-19 (1 works)