Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model.     

The Geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area.     

Critical autism studies: exploring epistemic dialogues and intersections,challenging dominant understandings of autism

In this paper we explore how our cultural contexts give rise to different kinds of knowledges of autism and examine how they are articulated, gain currency, and form the basis for policy, practice and political movements. We outline key tensions for the development of critical autism studies as an international, critical abilities approach. Our aim is not to offer a cross-cultural account of autism or to assume a coherence or universality of ‘autism’ as a singular diagnostic category/reality. Rather, we map the ways in which what is experienced and understood as autism, plays out in different cultural contexts, drawing on the notion of ‘epistemic communities’ to explore shifts in knowledge about autism, including concepts such as ‘neurodiversity’, and how these travel through cultural spaces. The paper explores two key epistemic tensions; the dominance of ‘neuro culture’ and dominant constructions of personhood and what it means to be human.     

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.     

An exploration of the relationships between student racial background and the school sub-contexts of office discipline referrals: a critical race theory analysis

A growing body of research indicates that exclusionary school discipline practices disproportionately impact students of color. Some scholars have theorized that racial disparities likely vary across school sub-contexts, as implicit bias in perceptions of student behavior may be more influential in locations where students and adults have weaker relationships (e.g. bathrooms and hallways, compared to the classroom). Guided by Critical Race Theory, this study used administrative data from a large urban school district (n = 20,166 discipline incidents, 9,170 students, and 185 schools) to consider the relationship between student race and the locations where youth are disciplined. Results indicate that Black, Latino/a, and Multiracial youth were no more likely than White students to have a discipline incident take place outside the classroom. These findings suggest attention is needed to the role of systemic bias and colorblind policies and practices in discipline disparities.     

Latent Tuberculosis Infection Screening Acceptability among Migrant Farmworkers

Latent tuberculosis (TB) infection is routinely diagnosed using the tuberculin skin test (TST). New methods of detection more specific than TST such as QuantiFERON TB Gold In‐Tube (QFT‐GIT) have been developed but evidence remains limited on their acceptability among migrant farmworkers. This article examined the acceptability of screening tests among migrant farmworkers working on the Arizona‐Mexico border. We conducted a cross‐sectional survey of migrant farmworkers via questionnaire. Of 83 participants interviewed, 53 (63.9%) believed that TB was a serious disease that could result in death and 59 (71.1%) considered TB a health concern in their community. Sixty‐four participants (77.1%) rated QFT‐GIT test as performing better than TST. Our study demonstrates preference for QFT‐GIT results over TST, and highlights migrant farmworkers’ considering themselves at risk of TB and TB as a health concern. Policies that create easy access and culturally appropriate, affordable healthcare for this vulnerable population should be encouraged.     

The Impact of Population,Contact, and Spatial Heterogeneity on Epidemic Model Predictions

Our objective was to evaluate the effect that complexity in the form of different levels of spatial, population, and contact heterogeneity has in the predictions of a mechanistic epidemic model. A model that simulates the spatiotemporal spread of infectious diseases between animal populations was developed. Sixteen scenarios of foot‐and‐mouth disease infection in cattle were analyzed, involving combinations of the following factors: multiple production‐types (PT) with heterogeneous contact and population structure versus single PT, random versus actual spatial distribution of population units, high versus low infectivity, and no vaccination versus preemptive vaccination. The epidemic size and duration was larger for scenarios with multiple PT versus single PT. Ignoring the actual unit locations did not affect the epidemic size in scenarios with multiple PT/high infectivity, but resulted in smaller epidemic sizes in scenarios using multiple PT/low infectivity. In conclusion, when modeling fast‐spreading epidemics, knowing the actual locations of population units may not be as relevant as collecting information on population and contact heterogeneity. In contrast, both population and spatial heterogeneity might be important to model slower spreading epidemic diseases. Our findings can be used to inform data collection and modeling efforts to inform health policy and planning.