scholarly journals Exploring the Association between Compliance with Measures to Prevent the Spread of COVID-19 and Big Five Traits with Bayesian Generalized Linear Model

2021 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Big Five ◽  
Generalized Linear Model ◽  
Regression Models ◽  
Large Scale ◽  
Negative Association ◽  
Data Driven ◽  
Big Five Traits ◽  
Bayesian Generalized Linear Model

Research has examined the association between people’s compliance with measures to prevent the spread of COVID-19 and personality traits. However, previous studies were conducted with relatively small-size datasets and employed frequentist analysis that does not allow data-driven model exploration. To address the limitations, a large-scale international dataset, COVIDiSTRESS Global Survey dataset, was explored with Bayesian generalized linear model that enables identification of the best regression model. The best regression models predicting participants’ compliance with Big Five traits were explored. The findings demonstrated first, all Big Five traits, except extroversion, were positively associated with compliance with general measures and distancing. Second, neuroticism, extroversion, and agreeableness were positively associated with the perceived cost of complying with the measures while conscientiousness showed negative association. The findings and the implications of the present study were discussed.

scholarly journals Trust in the research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset

2021 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Network Analysis ◽  
Generalized Linear Model ◽  
Large Scale ◽  
Preventive Measures ◽  
Scientific Research ◽  
Research Community ◽  
Data Driven ◽  
Prevention Measures ◽  
The Relationship ◽  
Bayesian Generalized Linear Model

In the present study, I explored the relationship between people's trust in different agents related to prevention of spread of COVID-19 and their compliance with pharmaceutical and non-pharmaceutical preventive measures. The COVIDiSTRESSII Global Survey dataset, which was collected from international samples, was analysed to examine the aforementioned relationship across different countries. For data-driven exploration, network analysis and Bayesian generalized linear model (GLM) analysis were performed. The result from network analysis demonstrated that trust in the scientific research community was most central in the network of trust and compliance. In addition, the outcome from Bayesian GLM analysis indicated that the same factor, trust in the scientific research community, was most fundamental in predicting participants' intent to comply with both pharmaceutical and non-pharmaceutical preventive measures. I briefly discussed the implications of the findings, the importance of trust in the scientific research community in explaining people's compliance with measure to prevent spread of COVID-19.

scholarly journals Regional regression models of percentile flows for the contiguous US: Expert versus data-driven independent variable selection

2016 ◽  
Author(s):  
Geoffrey Fouad ◽  
André Skupin ◽  
Christina L. Tague
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Predictive Performance ◽  
Data Driven ◽  
Mean Annual Precipitation ◽  
Expert Assessment ◽  
Independent Variables ◽  
Regional Regression ◽  
Data Driven Approach ◽  
Small Set

Abstract. Percentile flows are statistics derived from the flow duration curve (FDC) that describe the flow equaled or exceeded for a given percent of time. These statistics provide important information for managing rivers, but are often unavailable since most basins are ungauged. A common approach for predicting percentile flows is to deploy regional regression models based on gauged percentile flows and related independent variables derived from physical and climatic data. The first step of this process identifies groups of basins through a cluster analysis of the independent variables, followed by the development of a regression model for each group. This entire process hinges on the independent variables selected to summarize the physical and climatic state of basins. Distributed physical and climatic datasets now exist for the contiguous United States (US). However, it remains unclear how to best represent these data for the development of regional regression models. The study presented here developed regional regression models for the contiguous US, and evaluated the effect of different approaches for selecting the initial set of independent variables on the predictive performance of the regional regression models. An expert assessment of the dominant controls on the FDC was used to identify a small set of independent variables likely related to percentile flows. A data-driven approach was also applied to evaluate two larger sets of variables that consist of either (1) the averages of data for each basin or (2) both the averages and statistical distribution of basin data distributed in space and time. The small set of variables from the expert assessment of the FDC and two larger sets of variables for the data-driven approach were each applied for a regional regression procedure. Differences in predictive performance were evaluated using 184 validation basins withheld from regression model development. The small set of independent variables selected through expert assessment produced similar, if not better, performance than the two larger sets of variables. A parsimonious set of variables only consisted of mean annual precipitation, potential evapotranspiration, and baseflow index. Additional variables in the two larger sets of variables added little to no predictive information. Regional regression models based on the parsimonious set of variables were developed using 734 calibration basins, and were converted into a tool for predicting 13 percentile flows in the contiguous US. Supplementary Material for this paper includes an R graphical user interface for predicting the percentile flows of basins within the range of conditions used to calibrate the regression models. The equations and performance statistics of the models are also supplied in tabular form.

scholarly journals Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

2020 ◽  
Vol 124 (6) ◽  
pp. 1588-1604
Author(s):  
Naixin Ren ◽  
Shinya Ito ◽  
Hadi Hafizi ◽  
John M. Beggs ◽  
Ian H. Stevenson
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Large Scale ◽  
Statistical Problem ◽  
Detection Methods ◽  
Multielectrode Array ◽  
Synaptic Connections ◽  
Synaptic Effects ◽  
Synapse Detection

Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

scholarly journals Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
Andreas Groll ◽  
Gunther Schauberger ◽  
Gerhard Tutz
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Linear Model ◽  
Poisson Regression ◽  
Generalized Linear Model ◽  
Poisson Regression Model ◽  
World Cup ◽  
Fifa World Cup ◽  
Specific Effects ◽  
World Champion

AbstractIn this article an approach for the analysis and prediction of international soccer match results is proposed. It is based on a regularized Poisson regression model that includes various potentially influential covariates describing the national teams’ success in previous FIFA World Cups. Additionally, within the generalized linear model (GLM) framework, also differences of team-specific effects are incorporated. In order to achieve variable selection and shrinkage, we use tailored Lasso approaches. Based on preceding FIFA World Cups, two models for the prediction of the FIFA World Cup 2014 are fitted and investigated. Based on the model estimates, the FIFA World Cup 2014 is simulated repeatedly and winning probabilities are obtained for all teams. Both models favor the actual FIFA World Champion Germany.

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