scholarly journals The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App

2020 ◽  
Vol 11 ◽  
Author(s):  
Sarah Depaoli ◽  
Sonja D. Winter ◽  
Marieke Visser
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Study ◽  
Bayesian Methods ◽  
Multiple Regression Model ◽  
Specific Model ◽  
Estimation Methods ◽  
Prior Distributions ◽  
Shiny App ◽  
Novice Users ◽  
The Impact

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad (e.g., undergraduate or graduate students, faculty, and other researchers) and can include those with limited exposure to Bayesian methods or the specific model presented here.

How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS

2005 ◽  
Vol 24 (15) ◽  
pp. 2401-2428 ◽  
Author(s):  
Paul C. Lambert ◽  
Alex J. Sutton ◽  
Paul R. Burton ◽  
Keith R. Abrams ◽  
David R. Jones
Keyword(s):  
Simulation Study ◽  
Prior Distributions ◽  
The Impact

scholarly journals Dangers of the Defaults: A Tutorial on the Impact of Default Priors When Using Bayesian SEM With Small Samples

2020 ◽  
Vol 11 ◽  
Author(s):  
Sanne C. Smid ◽  
Sonja D. Winter
Keyword(s):  
Sample Size ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Small Sample ◽  
Small Samples ◽  
Informative Priors ◽  
Prior Distributions ◽  
Shiny App ◽  
Technical Discussion ◽  
The Impact

When Bayesian estimation is used to analyze Structural Equation Models (SEMs), prior distributions need to be specified for all parameters in the model. Many popular software programs offer default prior distributions, which is helpful for novel users and makes Bayesian SEM accessible for a broad audience. However, when the sample size is small, those prior distributions are not always suitable and can lead to untrustworthy results. In this tutorial, we provide a non-technical discussion of the risks associated with the use of default priors in small sample contexts. We discuss how default priors can unintentionally behave as highly informative priors when samples are small. Also, we demonstrate an online educational Shiny app, in which users can explore the impact of varying prior distributions and sample sizes on model results. We discuss how the Shiny app can be used in teaching; provide a reading list with literature on how to specify suitable prior distributions; and discuss guidelines on how to recognize (mis)behaving priors. It is our hope that this tutorial helps to spread awareness of the importance of specifying suitable priors when Bayesian SEM is used with small samples.

COVID-19 effective reproduction number determination: an application, and a review of issues and influential factors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Luis Alfredo Bautista Balbás ◽  
Mario Gil Conesa ◽  
Blanca Bautista Balbás ◽  
Gil Rodríguez Caravaca
Keyword(s):  
Sensitivity Analysis ◽  
Web Application ◽  
Reproduction Number ◽  
Infected Individual ◽  
National Level ◽  
Conclusive Evidence ◽  
Influential Factors ◽  
Estimation Methods ◽  
Serial Interval ◽  
The Impact

Abstract Objectives An essential indicator of COVID-19 transmission is the effective reproduction number (R t ), the number of cases which an infected individual is expected to infect at a particular point in time; curves of the evolution of R t over time (transmission curves) reflect the impact of preventive measures and whether an epidemic is controlled. Methods We have created a Shiny/R web application (https://alfredob.shinyapps.io/estR0/) with user-selectable features: open data sources with daily COVID-19 incidences from all countries and many regions, customizable preprocessing options (smoothing, proportional increment, etc.), different MonteCarlo-Markov-Chain estimates of the generation time or serial interval distributions and state-of-the-art R t estimation frameworks (EpiEstim, R 0). This application could be used as a tool both to obtain transmission estimates and to perform interactive sensitivity analysis. We have analyzed the impact of these factors on transmission curves. We also have obtained curves at the national and sub-national level and analyzed the impact of epidemic control strategies, superspreading events, socioeconomic factors and outbreaks. Results Reproduction numbers showed earlier anticipation compared to active prevalence indicators (14-day cumulative incidence, overall infectivity). In the sensitivity analysis, the impact of different R t estimation methods was moderate/small, and the impact of different serial interval distributions was very small. We couldn’t find conclusive evidence regarding the impact of alleged superspreading events. As a limitation, dataset quality can limit the reliability of the estimates. Conclusions The thorough review of many examples of COVID-19 transmission curves support the usage of R t estimates as a robust transmission indicator.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

scholarly journals Robust Inferences from a Before-and-After Study with Multiple Unaffected Control Groups

2013 ◽  
Vol 1 (2) ◽  
pp. 209-234 ◽  
Author(s):  
Pengyuan Wang ◽  
Mikhail Traskin ◽  
Dylan S. Small
Keyword(s):  
Sensitivity Analysis ◽  
Control Groups ◽  
Two Stage ◽  
Time Effects ◽  
Unaffected Control ◽  
Group Time ◽  
Before And After ◽  
Group Variation ◽  
The Impact ◽  
Before And After Study

AbstractThe before-and-after study with multiple unaffected control groups is widely applied to study treatment effects. The current methods usually assume that the control groups’ differences between the before and after periods, i.e. the group time effects, follow a normal distribution. However, there is usually no strong a priori evidence for the normality assumption, and there are not enough control groups to check the assumption. We propose to use a flexible skew-t distribution family to model group time effects, and consider a range of plausible skew-t distributions. Based on the skew-t distribution assumption, we propose a robust-t method to guarantee nominal significance level under a wide range of skew-t distributions, and hence make the inference robust to misspecification of the distribution of group time effects. We also propose a two-stage approach, which has lower power compared to the robust-t method, but provides an opportunity to conduct sensitivity analysis. Hence, the overall method of analysis is to use the robust-t method to test for the overall hypothesized range of shapes of group variation; if the test fails to reject, use the two-stage method to conduct a sensitivity analysis to see if there is a subset of group variation parameters for which we can be confident that there is a treatment effect. We apply the proposed methods to two datasets. One dataset is from the Current Population Survey (CPS) to study the impact of the Mariel Boatlift on Miami unemployment rates between 1979 and 1982.The other dataset contains the student enrollment and grade repeating data in West Germany in the 1960s with which we study the impact of the short school year in 1966–1967 on grade repeating rates.

scholarly journals A Model for the Thermal Behaviour of an Offshore Cable Installed in a J-Tube

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 31
Author(s):  
Jeremy Arancio ◽  
Ahmed Ould El Moctar ◽  
Minh Nguyen Tuan ◽  
Faradj Tayat ◽  
Jean-Philippe Roques
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Model ◽  
Thermal Behaviour ◽  
Energy Production ◽  
Power Transmission ◽  
Temperature Fields ◽  
Sobol Indices ◽  
Lumped Element ◽  
Thermal Phenomena ◽  
The Impact

In the race for energy production, supplier companies are concerned by the thermal rating of offshore cables installed in a J-tube, not covered by IEC 60287 standards, and are now looking for solutions to optimize this type of system. This paper presents a numerical model capable of calculating temperature fields of a power transmission cable installed in a J-tube, based on the lumped element method. This model is validated against the existing literature. A sensitivity analysis performed using Sobol indices is then presented in order to understand the impact of the different parameters involved in the heating of the cable. This analysis provides an understanding of the thermal phenomena in the J-tube and paves the way for potential technical and economic solutions to increase the ampacity of offshore cables installed in a J-tube.

scholarly journals The Impact of Work Characteristics on Social Distancing: Implications at the Time of COVID-19

2021 ◽  
Vol 18 (10) ◽  
pp. 5074
Author(s):  
Keisuke Kokubun ◽  
Yoshinori Yamakawa
Keyword(s):  
Information Processing ◽  
Multiple Regression ◽  
Explanatory Power ◽  
Work Conditions ◽  
Multiple Regression Model ◽  
Social Characteristics ◽  
Work Characteristics ◽  
Social Distancing ◽  
Using Data ◽  
The Impact

The coronavirus disease (COVID-19) continues to spread globally. While social distancing has attracted attention as a measure to prevent the spread of infection, some occupations find it difficult to implement. Therefore, this study aims to investigate the relationship between work characteristics and social distancing using data available on O*NET, an occupational information site. A total of eight factors were extracted by performing an exploratory factor analysis: work conditions, supervisory work, information processing, response to aggression, specialization, autonomy, interaction outside the organization, and interdependence. A multiple regression analysis showed that interdependence, response to aggression, and interaction outside the organization, which are categorized as ”social characteristics,” and information processing and specialization, which are categorized as “knowledge characteristics,” were associated with physical proximity. Furthermore, we added customer, which represents contact with the customer, and remote working, which represents a small amount of outdoor activity, to our multiple regression model, and confirmed that they increased the explanatory power of the model. This suggests that those who work under interdependence, face aggression, and engage in outside activities, and/or have frequent contact with customers, little interaction outside the organization, and little information processing will have the most difficulty in maintaining social distancing.

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