“I Really Belong Here”: Civic Capacity-Building among Returning Citizens

This article focuses on unlikely movement actors whose civic engagement has been understudied: people with criminal records (“returning citizens”). We present findings from 18 months of ethnographic research with members (leaders) of Fighting to Overcome Records and Create Equality (FORCE), a civic group led by returning citizens. FORCE leaders received institutional support from Community Renewal Society (CRS), a larger faith and community-based organization, to lead a rights reform movement in Chicago. Findings suggest that FORCE leaders constructed notions of kinship, recognition, and power through civic capacity-building efforts—and that social belonging was core to such capacity-building efforts. While bonding social belonging occurred as FORCE leaders formed kinship with people facing similar social and economic marginality, bridging social belonging emerged as leaders felt recognized by CRS staff organizers, affiliates, and elected officials. Bonding and bridging social belonging enabled FORCE leaders, who faced constant social exclusion in society, to experience much needed kinship, recognition, and power. Future studies should continue to uncover how local capacity-building processes have life-changing relational effects on movement participants from socially and economically marginalized groups.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Hard thresholding regression

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ?₀‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...     

Establishing Safety in Emotionally Focused Couple Therapy: A Single‐Case Process Study

Emotionally Focused Couple Therapy (EFT) is an effective approach to working with distressed couples. It is not only effective in reducing relationship distress, but also in successfully maintaining gains over time. In the current study, we sought to understand the therapy processes that result in the creation of safety during stage one of EFT. Safety is a key ingredient in stage one, setting the stage for deeper interpersonal and intrapsychic work in later stages of treatment. In order to achieve the study aim, we analyzed video recordings of the first six sessions of a single couple case from an EFT randomized controlled trial. Using a theory building case study approach, results reveal that all expected components related to the creation of safety were achieved. Additional findings from our analyses indicate that the therapist was highly active, especially in seizing key moments in the therapy provided by this particular case, which shifted the couple in the direction of safety and deescalated their interactional cycle. Implications of key findings are discussed.     

Is Sexual Abuse a Unique Predictor of Sexual Risk Behaviors,Pregnancy, and Motherhood in Adolescence?

This study tested sexual abuse as a unique predictor of subsequent adolescent sexual behaviors, pregnancy, and motherhood when in company with other types of maltreatment (physical abuse, neglect) and alternative behavioral, family, and contextual risk factors in a prospective, longitudinal study of maltreated (n = 275) and comparison (n = 239) nulliparous females aged 14–19 years old assessed annually through 19 years old. Hierarchical regression was used to disentangle risk factors that account for the associations of maltreatment type on risky sexual behaviors at 19 years old, adolescent pregnancy, and adolescent motherhood. Findings indicate that sexual and physical abuse remain significant predictors of risky sexual behaviors, and that sexual abuse remains a significant predictor of adolescent motherhood when alternative explanatory variables are controlled.