scholarly journals Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia

2020 ◽  
Vol 17 (23) ◽  
pp. 8883
Author(s):  
Liadira Kusuma Widya ◽  
Chin-Yu Hsu ◽  
Hsiao-Yun Lee ◽  
Lalu Muhamad Jaelani ◽  
Shih-Chun Candice Lung ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Variable Selection ◽  
Paddy Field ◽  
Prediction Models ◽  
Selection Procedure ◽  
Predictor Variables ◽  
Weighted Regression ◽  
Variable Selection Procedure ◽  
Monitoring Stations

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49–0.50 for PM10 and 0.46–0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.

scholarly journals A pseudo knockoff filter for correlated features

2018 ◽  
Vol 8 (2) ◽  
pp. 313-341
Author(s):  
Jiajie Chen ◽  
Anthony Hou ◽  
Thomas Y Hou
Keyword(s):  
Variable Selection ◽  
False Discovery Rate ◽  
Numerical Experiments ◽  
Selection Procedure ◽  
Numerical Examples ◽  
False Discovery ◽  
Variable Selection Procedure ◽  
Partial Analysis ◽  
False Discovery Proportion

Abstract In Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. Inspired by the work by Barber & Candès (2015, Ann. Statist., 43, 2055–2085), we propose a pseudo knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso statistic for the numerical examples that we consider in this paper. Although we cannot establish rigourous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform false discovery proportion bound and an expectation inequality.

scholarly journals A Variable Selection Procedure for X-ray Diffraction Phase Analysis

2007 ◽  
Vol 61 (12) ◽  
pp. 1398-1403 ◽  
Author(s):  
Daewon Lee ◽  
Hyeseon Lee ◽  
Chi-Hyuck Jun ◽  
Chang Hwan Chang
Keyword(s):  
Variable Selection ◽  
Phase Analysis ◽  
Selection Procedure ◽  
X Ray Diffraction ◽  
X Ray ◽  
Variable Selection Procedure

scholarly journals Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2020 ◽  
Vol 3 (1) ◽  
pp. 66-80 ◽  
Author(s):  
Sierra A. Bainter ◽  
Thomas G. McCauley ◽  
Tor Wager ◽  
Elizabeth A. Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Multiple Testing ◽  
Selection Procedure ◽  
Experimental Pain ◽  
Bayesian Variable Selection ◽  
Large Set ◽  
Web Based ◽  
Stochastic Search Variable Selection ◽  
Variable Selection Procedure ◽  
Multivariate Relationships

Frequently, researchers in psychology are faced with the challenge of narrowing down a large set of predictors to a smaller subset. There are a variety of ways to do this, but commonly it is done by choosing predictors with the strongest bivariate correlations with the outcome. However, when predictors are correlated, bivariate relationships may not translate into multivariate relationships. Further, any attempts to control for multiple testing are likely to result in extremely low power. Here we introduce a Bayesian variable-selection procedure frequently used in other disciplines, stochastic search variable selection (SSVS). We apply this technique to choosing the best set of predictors of the perceived unpleasantness of an experimental pain stimulus from among a large group of sociocultural, psychological, and neurobiological (functional MRI) individual-difference measures. Using SSVS provides information about which variables predict the outcome, controlling for uncertainty in the other variables of the model. This approach yields new, useful information to guide the choice of relevant predictors. We have provided Web-based open-source software for performing SSVS and visualizing the results.

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