Expanding the Bayesian structural equation, multilevel and mixture models to logit, negative-binomial, and nominal variables

2021 ◽  
pp. 1-16
Author(s):  
Tihomir Asparouhov ◽  
Bengt Muthén
Keyword(s):  
Mixture Models ◽  
Structural Equation ◽  
Negative Binomial ◽  
Nominal Variables

Anti-Blackness is the American Way: Assessing the Relationship Between Chattel Slavery, Lynchings, & Police Violence During the Civil Rights Movement

2022 ◽  
pp. 215336872110732
Author(s):  
Courtney M. Echols
Keyword(s):  
Civil Rights ◽  
Civil Rights Movement ◽  
Structural Equation ◽  
Negative Binomial ◽  
Equation Modeling ◽  
Future Research ◽  
Secondary Source ◽  
Police Violence ◽  
Chattel Slavery ◽  
Original Dataset

Research finds that historical anti-Black violence helps to explain the spatial distribution of contemporary conflict, inequality, and violence in the U.S. Building on this research, the current study examined the spatial relationship between chattel slavery in 1860, lynchings of Black individuals between 1882 and 1930, and anti-Black violence during the Civil Rights Movement era in which police or other legal authorities were implicated. I draw on an original dataset of over 300 events of police violence that occurred between 1954 and 1974 in the sample state of Louisiana, and that was compiled from a number of primary and secondary source documents that were themselves culled from archival research conducted in the state. Path analysis was then employed using negative binomial generalized structural equation modeling in order to assess the direct and indirect effects of these racially violent histories. The implications for social justice, public policy, and future research are also discussed. Keywords Slavery, lynchings, anti-Black violence, civil rights movement, police

Sex, Race, and Place

2018 ◽  
Vol 55 (4) ◽  
pp. 493-537
Author(s):  
Lyndsay N. Boggess ◽  
Ráchael A. Powers ◽  
Alyssa W. Chamberlain
Keyword(s):  
Race And Ethnicity ◽  
Criminal Behavior ◽  
Structural Equation ◽  
Negative Binomial ◽  
Equation Modeling ◽  
Structural Factors ◽  
Neighborhood Structure ◽  
Modeling Framework ◽  
Male And Female ◽  
Race Ethnicity

Objectives: We draw upon theories of social disorganization, strain, and subculture of violence to examine how sex and race/ethnicity intersect to inform nonlethal violent offending at the macrolevel. Methods: Using neighborhood-level incidents, we examine (1) the structural correlates of male and female nonlethal violence and (2) whether ecological conditions have variable impacts on the prevalence of White, Black, and Latino male and female offenses above and beyond differential exposure to disadvantage. We use multivariate negative binomial regression within a structural equation modeling framework which allows for the examination of the same set of indicator variables on more than one dependent variable simultaneously while accounting for covariance between the dependent variables. Results: We find few significant differences in the salience of disadvantage on female and male violence across race and ethnicity although some differences emerge for White men and women. Structural factors are largely sex invariant within race and ethnicity. Conclusions: Despite expectations that disadvantage would have differential effects across sex and race/ethnicity, we uncover only minor differences. This suggests that structural effects are more invariant than variant across subgroups and highlights the importance of investigating both similarities and differences when examining neighborhood structure, intersectionality, and criminal behavior.

scholarly journals Mixture Modeling for Lifespan Developmental Research

2019 ◽  
Author(s):  
Alexandre J.S. Morin ◽  
David Litalien
Keyword(s):  
Mixture Models ◽  
Structural Equation ◽  
Equation Modeling ◽  
Modeling Framework ◽  
Mixture Regression ◽  
Growth Mixture ◽  
Multiple Group ◽  
Latent Curve ◽  
Latent Curve Models ◽  
Over Time

As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.

scholarly journals Robustness of Poisson Mixture models in identifying risk factors for Under-Five mortality in Nigeria

2018 ◽  
Vol 4 (2) ◽  
pp. 141-154
Author(s):  
Kolawole S. Oritogun ◽  
Elijah A. Bamgboye
Keyword(s):  
Risk Factors ◽  
Birth Order ◽  
Mixture Models ◽  
Maternal Education ◽  
Negative Binomial ◽  
National Level ◽  
Poisson Model ◽  
Information Criteria ◽  
Poisson Mixture ◽  
Under Five

Background: Estimates of Under-Five mortality (U5M) have taken advantage of indirect methods but U5M risk factors have been identified using fixed statistical models with little considerations for the potentials of mixture models. Mixture models such as Poisson-Mixture models exhibit flexibility tendency, which is an attribute of robustness lacking in fixed models. Objective: To examine the robustness of Poisson-Mixture models in identifying reliable determinants of U5M. Methods: The data on 18,855 women used in this study were obtained from the 2008 Nigeria Demographic and Health Survey (NDHS). Six different Poisson-Mixture models namely: Poisson (PO), Zero-Inflated Poisson (ZIP), Poisson Hurdle (PH), Negative Binomial (NBI), Zero-Inflated Negative Binomial (ZINBI) and Negative Binomial Hurdle (NBIH) were fitted separately to the data. The Akaike Information Criteria (AIC) and diagnostic check for normality were used to select robust models. All tests were conducted at p = 0.05. Results: The models and AIC values for U5M were: 38763.47 (PO), 38654.55 (ZIP), 44270.77 (PH), 38526.26 (NBI), 38513.71 (ZINBI) and 44269.30 (NBIH). The PO, ZIP, PH and NBIH met normality test criteria, and the ZIP model was of best fit. The model identified breastfeeding, paternal education, toilet type, maternal education, place of delivery, birth-order and antenatal-visits as significant determinants of U5M at the national level. Conclusion: The Zero-Inflated Poisson model provided the best robust estimates of Under-five Mortality in Nigeria, while maternal education and birth-order were identified as the most important determinants. The Poisson-mixture models are recommended for modelling Under-five Mortality in Nigeria.

Commitment to Public Values, Charismatic Leadership Attributions, and Employee Turnover in Street-level Bureaucracies

2020 ◽  
Author(s):  
Gustavo M Tavares ◽  
Filipe Sobral ◽  
Bradley E Wright
Keyword(s):  
Social Identity Theory ◽  
Employee Turnover ◽  
Structural Equation ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Charismatic Leadership ◽  
Equation Modeling ◽  
Public Values ◽  
Street Level ◽  
Public Leaders

Abstract Public values (PV) are receiving growing attention in public administration research and scholars frequently stress the need for public leaders to commit to and promote PV to protect the public interest and build citizens’ trust in government. However, the relationship between public leaders’ commitment to PV and intra-organizational, behavioral outcomes has received much less theoretical and empirical attention. To help address this gap, we draw on the social identity theory of leadership to propose that leaders in street-level bureaucracies who are perceived to be committed to PV are also more likely to be perceived as charismatic leaders and that these leadership attributions will be associated with lower employee turnover, especially in more stressful work contexts. We test our hypotheses with OLS and negative binomial regression. Additional mediation tests were conducted with structural equation modeling. Based on a sample of 87 public organizations and 874 participants, our results reveal that perceived leader commitment to PV is positively associated with perceived charismatic leadership which, in turn, is associated with lower employee turnover in more stressful and demanding work environments. This study brings more publicness to public leadership studies and can inform public leaders on how to develop more engaging and inspirational forms of leadership with their constituencies.

scholarly journals Goodness of fit checks for binomial N-mixture models

2017 ◽  
Author(s):  
Jonas Knape ◽  
Debora Arlt ◽  
Frédéric Barraquand ◽  
Åke Berg ◽  
Mathieu Chevalier ◽  
...  
Keyword(s):  
Mixture Models ◽  
Mixture Model ◽  
Binomial Distribution ◽  
Detection Probability ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
List Type ◽  
Computationally Efficient ◽  
Abundance Distribution ◽  
Lack Of Fit

AbstractBinomial N-mixture models are commonly applied to analyze population survey data. By estimating detection probabilities, N-mixture models aim at extracting information about abundances in terms of actual and not just relative numbers. This separation of detection probability and abundance relies on parametric assumptions about the distribution of individuals among sites and of detections of individuals among repeat visits to sites. Current methods for checking assumptions are limited, and their computational complexity have hindered evaluations of their performances.We develop computationally efficient graphical goodness of fit checks and measures of overdispersion for binomial N-mixture models. These checks are illustrated in a case study, and evaluated in simulations under two scenarios. The two scenarios assume overdispersion in the abundance distribution via a negative binomial distribution or in the detection probability via a beta-binomial distribution. We evaluate the ability of the checks to detect lack of fit, and how lack of fit affects estimates of abundances.The simulations show that if the parametric assumptions are incorrect there can be severe biases in estimated abundances: negatively if there is overdispersion in abundance relative to the fitted model and positively if there is overdispersion in detection. Our goodness of fit checks performed well in detecting lack of fit when the abundance distribution is overdispersed, but struggled to detect lack of fit when detections were overdispersed. We show that the inability to detect lack of fit due to overdispersed detection is caused by a fundamental similarity between N-mixture models with beta-binomial detections and N-mixture models with negative binomial abundances.The strong biases in estimated abundances that can occur in the binomial N-mixture model when the distribution of individuals among sites, or the detection model, is mis-specified implies that checking goodness of fit is essential for sound inference in ecological studies that use these methods. To check the assumptions we provide computationally efficient goodness of fit checks that are available in an R-package nmixgof. However, even when a binomial N-mixture model appears to fit the data well, estimates are not robust in the presence of overdispersion unless additional information about detection is collected.

scholarly journals A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems

2018 ◽  
Vol 12 (2) ◽  
Author(s):  
Amir T. Payandeh Najafabadi ◽  
Saeed MohammadPour
Keyword(s):  
Regression Model ◽  
Mixture Models ◽  
Mixture Model ◽  
Negative Binomial ◽  
Mixture Distribution ◽  
Third Party ◽  
Numerical Illustration ◽  
Mixture Regression ◽  
Pure Premium ◽  
Binomial Mixture

Abstract This article introduces a k-Inflated Negative Binomial mixture distribution/regression model as a more flexible alternative to zero-inflated Poisson distribution/regression model. An EM algorithm has been employed to estimate the model’s parameters. Then, such new model along with a Pareto mixture model have employed to design an optimal rate–making system. Namely, this article employs number/size of reported claims of Iranian third party insurance dataset. Then, it employs the k-Inflated Negative Binomial mixture distribution/regression model as well as other well developed counting models along with a Pareto mixture model to model frequency/severity of reported claims in Iranian third party insurance dataset. Such numerical illustration shows that: (1) the k-Inflated Negative Binomial mixture models provide more fair rate/pure premiums for policyholders under a rate–making system; and (2) in the situation that number of reported claims uniformly distributed in past experience of a policyholder (for instance $k_1=1$ and $k_2=1$ instead of $k_1=0$ and $k_2=2$). The rate/pure premium under the k-Inflated Negative Binomial mixture models are more appealing and acceptable.

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