Ben-Chang Shia
Dados Biográficos
| ID | 8920173 |
|---|---|
| NOME | Ben-Chang Shia |
| PRENOMES | Ben-Chang |
| SOBRENOME | Shia |
| ASSINATURA | SHIA B |
| AFILIAÇÕES | Taipei Medical University |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2020 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 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 …
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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)