“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.     

Sequential-type nonparametric test using Mann-Whitney statistics

The paper provides a nonparametric test for the identity of two continuous univariate distribution functions when observations are drawn in pairs from the populations, by adopting a sampling scheme which, using Mann-Whitney scores, generalizes the existing inverse binomial sampling technique. Some exact performance characteristics of the proposed test are formulated and compared numerically with existing competitors of the proposed test. The applicability of the proposed test is illustrated using real-life data.     

Controlling Type-I Error Rate in Monitoring Structural Changes Using Partially Sequential Procedures

In the present article, we develop some asymptotically power on partially sequential nonparametric tests for monitoring structural changes. Our test procedures are based on Wilcoxon score. We use the idea of curved stopping boundaries. We derive some exact results and perform simulation studies to provide various properties of the tests. We see that one of the proposed procedures significantly controls the Type I error rate. This procedure may be very effective for fluctuation monitoring. We illustrate the procedures by using real life data from the stock market.     

Incremental Validity of Stressful Life Experiences in Predicting Psychiatric Comorbidity Among Women in Substance Abuse Treatment

ABSTRACT

The dynamic contribution of stressful life experiences in predicting psychiatric comorbidity continues to challenge clinical practice and research. This study tested incremental validity of stressful life experiences related to psychiatric comorbidity among 128 young women in a Midwestern substance abuse treatment facility. Respondents reported low income, homelessness, and health and mental health issues. Using the Addiction Severity Index (ASI), Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition, Text Revision, and Stressful Life Experience (SLE) Screen as correlates, hierarchical linear regression demonstrated support for incremental validity of SLE uniquely accounting for 6.5% of variance in ASI psychiatric scores. Findings support future use of SLE in clinical settings for assessment and intervention purposes.     

Complete diallel crosses plans through balanced incomplete block designs

The present investigation involves the methods of construction of complete diallel cross plans using balanced incomplete block (BIB) designs. Furthermore, the analysis of complete diallel crosses plans are carried out to estimate the general combining ability of the ith line (i=1, r 2, r …, r v) where the intra- block analysis of the adjusted sum of squares for GCA and the unadjusted block sum of squares are also obtained, thereafter the relationship between the estimates of BIB design and the estimates of the GCA effect of CDC plan has been established. Moreover, it has also been shown that the complete diallel crosses design obtained through two BIB designs satisfying v1=b1= 4 5 1+3=v2=b2, r r1=2 5 1+1=r2=k1=k2 and 5 1= 5 2 are universally optimum. These results are further supported by a suitable example of each. However, the need of this study is to show that the analysis of the CDC plan is reducible to the analysis of generating the BIB design.     

A response-adaptive design in crossover trial

In the present work, whenever the response variables are binary, we frame an adaptive allocation rule for a two-treatment two-period crossover design in the presence of possible carry-over effects. The proposed rule is a combination of the play-the-winner and randomized play-the-winner rules. We study various properties of the proposed rule through asymptotics and simulations. Some related inferential problems are also considered. The proposed procedure is compared with some possible competitor.     

Time series analysis of categorical data using auto-odds ratio function

In this paper, we consider the auto-odds ratio function (AORF) as a measure of serial association for a stationary time series process of categorical data at two different time points. Numerical measures such as the autocorrelation function (ACF) have no meaningful interpretation, unless the time series data are numerical. Instead, we use the AORF as a measure of association to study the serial dependency of the categorical time series for both ordinal and nominal categories. Biswas and Song [Discrete-valued ARMA processes. Stat Probab Lett. 2009;79(17):1884–1889] provided some results on this measure for Pegram's operator-based AR(1) process with binary responses. Here, we extend this measure to more general set-ups, i.e. for AR(p) and MA(q) processes and for a general number of categories. We discuss how this method can effectively be used in parameter estimation and model selection. Following Weiß [Empirical measures of signed serial dependence in categorical time series. J Stat Comput Simul. 2011;81(4):411–429], we derive the large sample distribution of the estimator of the AORF under independent and identically distributed (iid) set-up. Some simulation results and two categorical data examples (one is ordinal and other nominal) are presented to illustrate the proposed method.     

Nonparametric estimation of 100(1 − p)% expected shortfall: p → 0 as sample size is increased

Expected shortfall (ES) is a well-known measure of extreme loss associated with a risky asset or portfolio. For any 0 < p < 1, the 100(1 ? p) percent ES is defined as the mean of the conditional loss distribution, given the event that the loss exceeds (1 ? p)th quantile of the marginal loss distribution. Estimation of ES based on asset return data is an important problem in finance. Several nonparametric estimators of the expected shortfall are available in the literature. Using Monte Carlo simulations, we compare the accuracy of these estimators under the condition that p → 0 as n → ∞ for several asset return time series models, where n is the sample size. Not much seems to be known regarding the properties of the ES estimators under this condition. For p close to zero, the ES measures an extreme loss in the right tail of the loss distribution of the asset or portfolio. Our simulations and real-data analysis provide insight into the effect of varying p with n on the performance of nonparametric ES estimators.