scholarly journals Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

2021 ◽  
Vol 7 (2) ◽  
pp. 4001-4010
Author(s):  
Sampson Twumasi-Ankrah ◽  
◽  
Michael Owusu ◽  
Simon Kojo Appiah ◽  
Wilhemina Adoma Pels ◽  
...  
Keyword(s):  
Model Selection ◽  
Model Selection Approach ◽  
Selection Approach

scholarly journals HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data

SoftwareX ◽  
2020 ◽  
Vol 12 ◽  
pp. 100559 ◽  
Author(s):  
Michael G. Leeming ◽  
Andrew P. Isaac ◽  
Luke Zappia ◽  
Richard A.J. O’Hair ◽  
William A. Donald ◽  
...  
Keyword(s):  
Model Selection ◽  
Drug Metabolites ◽  
Model Selection Approach ◽  
Selection Approach

scholarly journals Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 577
Author(s):  
Daisuke Murakami ◽  
Mami Kajita ◽  
Seiji Kajita
Keyword(s):  
Big Data ◽  
Model Selection ◽  
Regression Models ◽  
Large Scale ◽  
Spatial Regression ◽  
Varying Coefficients ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Spatial Regression Models ◽  
Spatially Varying

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.

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