Flexible estimation of serial correlation in nonlinear mixed models

In the conventional linear mixed-effects model, four structures can be distinguished: fixed effects, random effects, measurement error and serial correlation. The latter captures the phenomenon that the correlation structure within a subject depends on the time lag between two measurements. While the general linear mixed model is rather flexible, the need has arisen to further increase flexibility. In addition to work done in the area, we propose the use of spline-based modeling of the serial correlation function, so as to allow for additional flexibility. This approach is applied to data from a pre-clinical experiment in dementia which studied the eating and drinking behavior in mice.     

Independent Mental Health Advocacy: A Model of Social Work Advocacy?

ABSTRACT

Advocacy has received less attention in social work research than other aspects of social work practice. This paper draws attention to two tensions in social work advocacy; between worker-led advocacy and person-led advocacy, and between individual advocacy and system level advocacy. We argue that human-rights-based social workers must choose a person-led approach over a worker-led approach while advocating with both systems and individuals. This argument is made by drawing on findings of an evaluation of Independent Mental Health Advocacy (IMHA) in Victoria, Australia. It is shown that social work training had not prepared social workers for rights-based, person-led advocacy and that social workers in public mental health services were struggling to maintain the rights of people in their services even with assistance from IMHA.

IMPLICATIONS

• Independent Mental Health Advocacy (IMHA) is a model of advocacy influenced by social work theory and delivered in part by social-work-trained advocates.

• Social work training is not preparing social workers for person-led, human-rights-based advocacy.

• Public mental health social workers are struggling to maintain the rights of people in mental health services even with the support of external advocacy services.

     

Individualism Behind Collectivism: A Reflection from Saudi Volunteers

Volunteering is growing rapidly worldwide and has been recognized as a significant social force, contributing to social development. Motives for volunteering vary widely, ranging from collectivistic factors to individualistic ones. Collectivism is often identified as a main factor that contributes to volunteering, especially in collectivist societies. Our analysis shows that in Saudi Arabia—typically classified as a collectivist society—individualistic considerations such as learning skills, meeting friends, and releasing guilt mediate the effect that collectivistic motivations (e.g., prosocial personality and community identity) have on the decision of continuous volunteering. This finding is applicable to both males and females, to people in different forms of employment, across ages, and regardless of family members’ volunteering behavior, according to moderation analyses.     

Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data “double‐dipping” and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided.     

Gauss–Christoffel quadrature for inverse regression: applications to computer experiments

Sufficient dimension reduction (SDR) provides a framework for reducing the predictor space dimension in statistical regression problems. We consider SDR in the context of dimension reduction for deterministic functions of several variables such as those arising in computer experiments. In this context, SDR can reveal low-dimensional ridge structure in functions. Two algorithms for SDR—sliced inverse regression (SIR) and sliced average variance estimation (SAVE)—approximate matrices of integrals using a sliced mapping of the response. We interpret this sliced approach as a Riemann sum approximation of the particular integrals arising in each algorithm. We employ the well-known tools from numerical analysis—namely, multivariate numerical integration and orthogonal polynomials—to produce new algorithms that improve upon the Riemann sum-based numerical integration in SIR and SAVE. We call the new algorithms Lanczos–Stieltjes inverse regression (LSIR) and Lanczos–Stieltjes average variance estimation (LSAVE) due to their connection with Stieltjes’ method—and Lanczos’ related discretization—for generating a sequence of polynomials that are orthogonal with respect to a given measure. We show that this approach approximates the desired integrals, and we study the behavior of LSIR and LSAVE with two numerical examples. The quadrature-based LSIR and LSAVE eliminate the first-order algebraic convergence rate bottleneck resulting from the Riemann sum approximation, thus enabling high-order numerical approximations of the integrals when appropriate. Moreover, LSIR and LSAVE perform as well as the best-case SIR and SAVE implementations (e.g., adaptive partitioning of the response space) when low-order numerical integration methods (e.g., simple Monte Carlo) are used.

     

Global kernel estimator and test of varying‐coefficient autoregressive model

We propose a varying‐coefficient autoregressive model that contains additive models, varying‐ coefficient models, partially linear models and low‐dimensional interaction models as special cases. A global kernel backfitting method is proposed for the estimation and inference of parameters and unknown functions in this model. Key large‐sample results are established, including estimation consistency, asymptotic normality and the generalized likelihood ratio test for parameters and non‐parametric functions. The proposed methodology is examined by simulation studies and applied to examine the relationship between suicide news reports in the three leading newspapers and the daily number of suicides in Taiwan. The relationship between the media reporting and suicide incidence has been established and explored. The Canadian Journal of Statistics 47: 487–519; 2019 © 2019 Statistical Society of Canada     

Between Church and State: a Christian Brotherhood’s Faithful Claims to Secularity in Mexico City

Qualitative Sociology - The Good News Businessmen’s Brotherhood (GNBB) is a Christian organization with evangelical commitments, charismatic practices, and whose members insist that their...     

Approaches to the risk of riding motorcycles: reflections on the problem of reconciling statistical risk assessment and motorcyclists' own reasons for riding

The manners in which motorcyclists and road safety experts assess motorcycling diverge widely. Experts view it as an extremely risky venture and imply that only the foolhardy would engage in it. Our own survey research appears to support this view. And yet, motorcyclists disagree with this assessment and construct their own theory of risk. One in which blame is laid at the door of the car culture and accidents something that can be overcome by road skills. The view of the experts concern with their risk taking becomes redefined as the attempted imposition of social regulation. We attempt to show how these different approaches to risk can be theoretically reconciled. We argue that motorcycling accidents need to be seen as real physical events, the understanding of which is socially contested.     

Charismatic leadership: Eliciting and channeling follower emotions

Research on charismatic leadership has been criticized for the ambiguity of its central construct. Attempts to define and measure charisma have frequently treated it as a complex construct consisting of multiple components. However, little work has been done to develop a theoretical model that offers a parsimonious rationale explaining why certain leadership attributes are considered “charismatic” while others are not, or how these attributes combine to produce charismatic effects. Addressing these issues, we present a model that situates emotion as the primary variable in the charismatic process. We use recent research on the moral emotions to frame a theory of followership-relevant emotions (FREs) that describes how leaders use emotions such as compassion, admiration, and anger to compel their followers to act. We then discuss the Elicit-Channel (EC) model of charismatic leadership, positing that the charismatic relationship is a five-step, cyclical process. In the EC model, leaders elicit highly motivating emotions from their followers and then channel those emotions to produce action that, if successful, results in outcomes such as positive affect and trust. These outcomes then enable the leader to continue the cycle, eliciting emotion once more. We conclude by offering a research agenda, addressing potential methodological concerns, and discussing future directions.