Bootstrap tests: how many bootstraps?

In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize experimental randomness. Simulation experiments suggest that this procedure will work very well in practice.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

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.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

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.     

Developing critical and empathic capabilities in intercultural education through the VaKE approach

One of the core goals of intercultural education is to develop critical and empathic reflection on social justice, particularly as related to humanistic choices and how individuals can counteract exclusion. The present article analyses the Values and Knowledge Education (VaKE) approach, which is aimed at raising awareness about implicit value-oriented priorities in decision-making. Through the Human Development and Capability Approach, the applicability of VaKE’s didactic principles is analysed in relation to young people’s perspectives on resettlement in Europe. The VaKE method was used at a 2013 summer camp in Austria with a culturally diverse group of young Europeans who were presented with a dilemma story that highlighted the complex issue of providing assistance to asylum seekers. The participants engaged in various knowledge and moral viability checks that enabled them to engage with opposing arguments. The participants evidenced critical reflection and self-scrutiny, as well as affiliation and empathic imagination, regarding problems that are encountered by asylum seekers.     

Socializing Children About Family Structure: Perspectives of Lesbian and Gay Adoptive Parents

Despite increasing numbers of lesbian- and gay- (LG-) parent families, little research on LG-adoptive-parent families has examined parental beliefs and perceptions related to discussing their sexual minority status with their children. This study assessed 266 LG adoptive parents (160 lesbian mothers, 106 gay fathers) from the Modern Adoptive Families (MAF) study, a nationwide survey (2012–2013) of adoptive parents' pre- and post-placement experiences. A cultural socialization lens provided the framework for examining LG parenting beliefs and practices. Two measurement scales were developed to examine parents' perceptions and self-efficacy related to socializing their child about being in a sexual-minority-parent family. Results indicate that LG parents endorse the importance of unique socialization practices and generally feel confident engaging in these practices. Parent education, transracial adoption, endorsement of racial socialization, and socialization self-efficacy were positively associated with endorsement of socialization practices. Excellent reliability suggests the 2 scales have the potential of being psychometrically sound instruments with which to measure parental endorsement and related self-efficacy of socialization practices for families headed by sexual minority parents. Findings contribute to a deeper understanding of socialization and communication patterns in LG-headed families, especially those formed through adoption. Research, policy, and practice implications are discussed.