The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation

2008 ◽  
Author(s):  
Robert J. Franzese ◽  
Jude C. Hays
Keyword(s):  
Probit Model ◽  
Binary Outcomes ◽  
Spatial Probit

A Spatial Probit Modeling Approach to Account for Spatial Spillover Effects in Dichotomous Choice Contingent Valuation Surveys

2013 ◽  
Vol 45 (1) ◽  
pp. 53-63 ◽  
Author(s):  
John B. Loomis ◽  
Julie M. Mueller
Keyword(s):  
Contingent Valuation ◽  
Voting Behavior ◽  
Contingent Valuation Method ◽  
Probit Model ◽  
Spillover Effects ◽  
Dichotomous Choice ◽  
Spatially Correlated ◽  
Valuation Method ◽  
Spatial Probit ◽  
Unbiased Estimates

We present a demonstration of a Bayesian spatial probit model for a dichotomous choice contingent valuation method willingness-to-pay (WTP) questions. If voting behavior is spatially correlated, spatial interdependence exists within the data, and standard probit models will result in biased and inconsistent estimated nonbid coefficients. Adjusting sample WTP to population WTP requires unbiased estimates of the nonbid coefficients, and we find a $17 difference in population WTP per household in a standard vs. spatial model. We conclude that failure to correctly model spatial dependence can lead to differences in WTP estimates with potentially important policy ramifications.

A spatial probit model for fine-scale mapping of disease genes

2007 ◽  
Vol 31 (3) ◽  
pp. 252-260 ◽  
Author(s):  
Maria De Iorio ◽  
Claudio J. Verzilli
Keyword(s):  
Probit Model ◽  
Disease Genes ◽  
Fine Scale ◽  
Spatial Probit ◽  
Fine Scale Mapping

scholarly journals Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261737
Author(s):  
Jong Wook Lee ◽  
So Young Sohn
Keyword(s):  
Spatial Autocorrelation ◽  
Default Risk ◽  
Predictive Power ◽  
Credit Scoring ◽  
Probit Model ◽  
Peer To Peer ◽  
Empirical Results ◽  
Default Prediction ◽  
Spatial Probit ◽  
Relational Network

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.

Translocation Relative to Range

Methodology ◽  
2008 ◽  
Vol 4 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Michael Höfler
Keyword(s):  
Risk Difference ◽  
Binary Outcomes ◽  
Number Needed To Treat ◽  
Likert Scales ◽  
Psychological Science ◽  
The Absolute ◽  
The Difference ◽  
Variance Parameters ◽  
Standardized Index ◽  
Maximum Possible Magnitude

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohen’s d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohen’s d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohen’s d to avoid the problems related to the latter. In any case, the absolute increment should complement d.

Sociodemographic inequalities in lifetime treatment seeking and receipt for common mental health problems

2020 ◽  
Author(s):  
Christopher Rayner ◽  
Jonathan Richard Iain Coleman ◽  
Kirstin Lee Purves ◽  
Ewan Carr ◽  
Rosa Cheesman ◽  
...  
Keyword(s):  
Depressive Disorders ◽  
Mental Health Problems ◽  
Sociodemographic Factors ◽  
Treatment Seeking ◽  
Binary Outcomes ◽  
Treatment Access ◽  
Sociodemographic Correlates ◽  
Common Mental Health Problems ◽  
Poor Outcomes ◽  
Lifetime Treatment

Background: Anxiety and depressive disorders can be chronic and disabling, and are associated with poor outcomes. Whilst there are effective treatments, access to these is variable and only a fraction of those in need receive treatment. Aims: The primary aim was to investigate sociodemographic correlates of lifetime treatment access and unpick the relationships between socioeconomic features and treatment inequalities. As such, we aimed to identify groups at greatest risk of never accessing treatment and targets for intervention. Methods: We tested for sociodemographic factors associated with treatment access in UK Biobank participants with lifetime generalised anxiety or major depressive disorder, performing multivariable logistic regressions on two binary outcomes: treatment-seeking (n=33,704) and treatment receipt (n=28,940). Results: Treatment access was less likely in those who were male, from a UK ethnic minority background and who self-medicated with alcohol or drugs. Treatment access was more likely in those who reported use of self-help strategies, with lower income (<£30,000) and greater neighbourhood deprivation, as well as those with a university degree. Conclusion: This work on lifetime treatment seeking and receipt replicates known correlates of treatment receipt during time of treatment need. Our focus on treatment-seeking and receipt highlights two targets for improving treatment access. More work is required to understand the psychosocial barriers to treatment, which mediate the associations observed here.

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