Preservation properties for the Laplace transform ordering of residual lives

Several preservation results for the Laplace transform ordering of residual lives (Lt−rl) are given. In particular, we show that theTt−rl-order is preserved under convolutions, mixtures and weak convergence. Some examples of interest in reliability theory are also presented.     

On the relation between Wiener index and eccentricity of a graph

Journal of Combinatorial Optimization - The relation between the Wiener index W(G) and the eccentricity $$\varepsilon (G)$$ of a graph G is studied. Lower and upper bounds on W(G) in terms of...     

“Isn't it ironic…!?!” Mobility researchers go sedentary: A group auto-ethnography on collective coping and care in pandemic times

We moved places and places moved us, until force majeure detained us on the spot. Signed-up to be hyper-mobile Ph.D.-candidates, we became hyper-reflective pandemic intimates. We moved together into a space that felt safe, OUR safe space. Suspended. Did the pandemic open this door, or had this space always existed, even back in the old days? Probably the latter, although we were not sensitive enough to perceive it, too busy to push the door, too lonesome to CARE. Not attentive to its possibilities, not imaginative of its POWER, too confident to be capable of succeeding alone. Even if we might have secretly wished for this space to exist. The present piece of work, and JOY, might be described by others as a “side-step,” a “hobby project,” a “shadow activity.” For us, it is a recollection of shocks and wonders, a sentience of precious, ephemeral instances that last. We are a group of eight early career researchers who study global mobility and labor migration from a variety of disciplinary perspectives. With prior international mobility experience, we left our previous countries of residence in 2018 to join an EU-funded research project, whilst being located in different European cities. One could classify us, for example, as highly qualified, privileged migrants. The present paper is the outcome of a collaborative, auto-ethnographic study, conducted in 2020, in the midst of the Covid-19 pandemic, when we suddenly were forced not to travel anymore. We got together online every week to “refaire le monde,” and we conducted virtual, dialogical self-interrogations and group reflections. Based on an emic approach, in line with Chang et al. (2013), we applied an iterative process of data collection and analysis. Our weekly conversations naturally emerged as a safe space for exchange and understanding, as we were facing similar situations, despite staying at different places. Suddenly, as the privilege of “always being on the move,” “always socializing and networking” disappeared due to closed borders and pandemic threats, we experienced anxieties and isolation and had to re-evaluate our perceptions on life, work, and international mobility. The very purpose and meaning of our broader research endeavors and employment perspectives suddenly faded away. We realized more than ever before, what it means to us to be allowed to move, to travel freely across continents.     

Influence diagnostics in constrained general linear models

ABSTRACT

Constrained general linear models (CGLMs) have wide applications in practice. Similar to other data analysis, the identification of influential observations that may be potential outliers is an important step beyond in the CGLMs. We develop multiple case-deletion diagnostics for detecting influential observations in the CGLMs. The diagnostics are functions of basic building blocks: studentized residuals, error contrast matrix, and the inverse of the response variable covariance matrix. The basic building blocks are computed only once from the complete data analysis and provide information on the influence of the data on different aspects of the model fit. Computational formulas are given which make the procedures feasible. An illustrative example with a real data set is also reported.     

Sensitivity analysis in ordered and restricted parameter models

This paper deals with the problem of local sensitivity analysis in ordered parameter models. In addition to order restrictions, some constraints imposed on the parameters by the model and/or the data are considered. Measures for assessing how much a change in the data modifies the results and conclusions of a statistical analysis of these models are presented. The sensitivity measures are derived using recent results in mathematical programming. The estimation problem is formulated as a primal nonlinear programming problem, and the sensitivities of the parameter estimates as well as the objective function sensitivities with respect to data are obtained. They are very effective in revealing the influential observations in this type of models and in evaluating the changes due to changes in data values. The methods are illustrated by their application to a wide variety of examples of order-restricted models including ordered exponential family parameters, ordered multinomial parameters, ordered linear model parameters, ordered and data constrained parameters, and ordered functions of parameters.     

Topp–Leone generated family of distributions: Properties and applications

We introduce a new family of distributions based on a one-parameter distribution exhibiting bathtub-shaped hazard rates. We study the mathematical properties of the family and estimate its parameters by the method of maximum likelihood. Finally, the usefulness of the family is illustrated using a real dataset.     

An extensive power evaluation of some tests for the inverse Gaussian distribution

Many applications of the Inverse Gaussian distribution, including numerous reliability and life testing results are presented in statistical literature. The paper studies the problem of using entropy tests to examine the goodness of fit of an Inverse Gaussian distribution with unknown parameters. Some entropy tests based on different entropy estimates are proposed. Critical values of the test statistics for various sample sizes are obtained by Monte Carlo simulations. Type I error of the tests is investigated and then power values of the tests are compared with the competing tests against various alternatives. Finally, recommendations for the application of the tests in practice are presented.     

Perceived Cultural Distance in Healthcare in Immigrant Intercultural Medical Encounters

This article responds to the paucity of empirical research on the impact of patient-provider ethno-cultural distance and providers' cultural competence on the quality of healthcare service in migrant intercultural medical encounters in Australia. A cross-sectional study was conducted with 447 patients, consisting of 195 Caucasian and 252 non-Caucasian patients from six outpatient clinics in New South Wales, Australia. Structural equation modelling was used for data analysis. The findings demonstrated that cultural distance is an influential factor in perceived quality of outpatient care. Caucasian and non-Caucasian patients' perception of cultural distance between their physicians and themselves affected their service quality ratings of their physicians' professionalism, empathy and expertise. This is the first study on healthcare service quality in outpatient clinics in Australia for Caucasian and non-Caucasian patients. Policy implications suggest intervention strategies and the need for cultural competence education in medical and health curricula in medical schools and clinical practices.     

Application of Different Techniques of Factor Analysis to Develop Neighborhood Social Capital Rating Scale

Composite indicators are widely used to determine the ranking of countries, organizations or individuals in terms of overall performance on multiple criteria. Their calculation requires standardization of the individual statistical criteria and aggregation of the standardized indicators. These operations introduce a potential propagation effect of extreme values on the calculation of the composite indicator of all entities. In this paper, we propose robust composite indicators for which this propagation effect is limited. The approach uses winsorization based on a robust estimate of the distribution of the sub-indicators. It is designed such that the winsorization affects only the composite indicator rank but has no effect on the entities ranking in each sub-indicator. The simulation study documents the benefits of distribution-based winsorization in the presence of outliers. It leads to a ranking that is closer to the clean data ranking when compared to the ranking obtained using either no winsorization or the traditional winsorization based on empirical quantiles. In the empirical application, we illustrate the use of winsorization for ranking countries based on the United Nations Industrial Development Organization’s Competitive Industrial Performance index. We show that even though the sub-indicator ranking does not change, the robust winsorization approach has a material impact on the ranking of the composite indicator for countries with large discrepancies in the scores of the sub-indicators.

     

An efficient correction to the density-based empirical likelihood ratio goodness-of-fit test for the inverse Gaussian distribution

The inverse Gaussian (IG) distribution is widely used to model positively skewed data. An important issue is to develop a powerful goodness-of-fit test for the IG distribution. We propose and examine novel test statistics for testing the IG goodness of fit based on the density-based empirical likelihood (EL) ratio concept. To construct the test statistics, we use a new approach that employs a method of the minimization of the discrimination information loss estimator to minimize Kullback–Leibler type information. The proposed tests are shown to be consistent against wide classes of alternatives. We show that the density-based EL ratio tests are more powerful than the corresponding classical goodness-of-fit tests. The practical efficiency of the tests is illustrated by using real data examples.