Robust designs for first-order multiple design multivariate models

This paper deals with the methodology of obtaining designs that are robust to nonnormality of the error distribution for tests associated with first-order multiple design multivariate linear models.A sequential procedure is given for the generation of the proposed designs.     

Practical small sample inference for single lag subset autoregressive models

We propose a method for saddlepoint approximating the distribution of estimators in single lag subset autoregressive models of order one. By viewing the estimator as the root of an appropriate estimating equation, the approach circumvents the difficulty inherent in more standard methods that require an explicit expression for the estimator to be available. Plots of the densities reveal that the distributions of the Burg and maximum likelihood estimators are nearly identical. We show that one possible reason for this is the fact that Burg enjoys the property of estimation equation optimality among a class of estimators expressible as a ratio of quadratic forms in normal random variables, which includes Yule–Walker and least squares. By inverting a two-sided hypothesis test, we show how small sample confidence intervals for the parameters can be constructed from the saddlepoint approximations. Simulation studies reveal that the resulting intervals generally outperform traditional ones based on asymptotics and have good robustness properties with respect to heavy-tailed and skewed innovations. The applicability of the models is illustrated by analyzing a longitudinal data set in a novel manner.     

A Fragmented Discourse of Religious Leadership in France: Muslim Youth between Citizenship and Radicalization

Abstract

At a time when public debates about radicalization of Muslim youth in the West are taking center stage and when questions about “home-grown” security threats are increasing in the wake of a number of terrorist attacks in many émigré societies, this article provides fresh empirical insights from the perspective of religious leadership. It outlines a picture of a highly diverse Muslim religious landscape where competing religious discourses are struggling to attract and support Muslim youth facing social dislocation and identity crises within increasingly contested social milieus. The article argues that a typology of religious leadership is clearly emerging where a spectrum of faith-based orientations and religious practice emphasize, to different degrees, notions of attachment to universal ethics and individual agency. The fact that conservative, sometimes radical, interpretations of such contestations represent a minority of voices is heartening even though the actual damage by such minority is often disproportionate to its actual size within the so-called silent majority. The empirical insights provided by the religious leaders interviewed for this study offer hope that the future of Western Muslims is more positive than we are led to think, if the possibility of combining devout faith with local political engagement becomes a real and sustainable conduit towards social inclusion and intercultural understanding and if necessary support and understanding are extended by the host communities.     

Alone,together: On the benefits of Bayesian borrowing in a meta-analytic setting

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any $K\ge 1$ historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.