Ben-Chang Shia
Datos Biográficos
| ID | 8920173 |
|---|---|
| NOMBRE | Ben-Chang Shia |
| NOMBRES | Ben-Chang |
| APELLIDO | Shia |
| FIRMA | SHIA B |
| AFILIACIONES | Taipei Medical University |
| VERIFICADO | No |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2020 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2025 |
| ÍNDICE H | 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 …
Sin obras prominentes en esta página.
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 obras) · Mathematics (2 obras) · 2019-20 coronavirus outbreak (1 obras) · Artificial Intelligence (1 obras) · Bayesian probability (1 obras) · Betacoronavirus (1 obras) · Complex Systems and Time Series Analysis (1 obras) · Computational and Text Analysis Methods (1 obras) · Coronavirus (1 obras) · Coronavirus disease 2019 (COVID-19 (1 obras)