Does Cultural Difference Affect Investment–Cash Flow Sensitivity? Evidence from OECD Countries

We investigate the influence of national culture on corporate investment–cash flow sensitivity. We conjecture that national culture shapes managerial perceptions of information asymmetry and agency problems, thus impacting the investment–cash flow relationship. We document empirical evidence in support of our claim. By linking the investment–cash flow sensitivity to cultural differences, our findings show that, while collectivism has an attenuating influence, uncertainty avoidance, power distance and masculinity have a reinforcing effect on the relationship between cash flow and investment. Our results hold for a sample of 205,268 firm‐years across 24 OECD countries between 1990 and 2017, and are robust after accounting for alternative statistical approaches, sample compositions and measures of cultural dimensions, along with controls for institutional and governmental factors. In addition, by decomposing cash flow into uses and sources of funds in a dynamic multi‐equation model, where firms make financing and investment decisions jointly subject to the constraint that sources must equal uses of cash, we find that national culture shapes how firms react to changes in cash flow.     

A robust class of homoscedastic nonlinear regression models

In this paper, we examine a nonlinear regression (NLR) model with homoscedastic errors which follows a flexible class of two-piece distributions based on the scale mixtures of normal (TP-SMN) family. The objective of using this family is to develop a robust NLR model. The TP-SMN is a rich class of distributions that covers symmetric/asymmetric and lightly/heavy-tailed distributions and is an alternative family to the well-known scale mixtures of skew-normal (SMSN) family studied by Branco and Dey [35]. A key feature of this study is using a new suitable hierarchical representation of the family to obtain maximum-likelihood estimates of model parameters via an EM-type algorithm. The performances of the proposed robust model are demonstrated using simulated and some natural real datasets and also compared to other well-known NLR models.     

Estimation of average causal effect using the restricted mean residual lifetime as effect measure

Although mean residual lifetime is often of interest in biomedical studies, restricted mean residual lifetime must be considered in order to accommodate censoring. Differences in the restricted mean residual lifetime can be used as an appropriate quantity for comparing different treatment groups with respect to their survival times. In observational studies where the factor of interest is not randomized, covariate adjustment is needed to take into account imbalances in confounding factors. In this article, we develop an estimator for the average causal treatment difference using the restricted mean residual lifetime as target parameter. We account for confounding factors using the Aalen additive hazards model. Large sample property of the proposed estimator is established and simulation studies are conducted in order to assess small sample performance of the resulting estimator. The method is also applied to an observational data set of patients after an acute myocardial infarction event.     

Semiparametric regression for restricted mean residual life under right censoring

A mean residual life function (MRLF) is the remaining life expectancy of a subject who has survived to a certain time point. In the presence of covariates, regression models are needed to study the association between the MRLFs and covariates. If the survival time tends to be too long or the tail is not observed, the restricted mean residual life must be considered. In this paper, we propose the proportional restricted mean residual life model for fitting survival data under right censoring. For inference on the model parameters, martingale estimating equations are developed, and the asymptotic properties of the proposed estimators are established. In addition, a class of goodness-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and the approach is applied to a set of real life data collected from a randomized clinical trial.     

Veiling in Fear or in Faith? Meanings of the Hijab to Practicing Muslim Wives and Husbands in USA

This article examines the Muslim practice of wearing the hijab—the veiling and covering of a woman’s head and body. More specifically, this study aims to present insiders’ perspectives regarding the personal understandings of the hijab among 20 wives and 20 husbands (n?=?40) in religious Shia and Sunni Muslim families living in USA. Qualitative analysis yielded three emergent themes: (1) The hijab as a symbol of religious commitment; (2) the hijab as a tool of protection, rather than oppression, for women and families; and (3) two different views of Muslims’ reasoning behind the hijab. These data suggest that, as perceived by “insider” participants, the hijab has perceived benefits for religious Muslim families, although counterexamples and concerns are also expressed.     

On a new positive dependence concept based on the conditional mean inactivity time order

In this paper, we introduce a new positive dependence concept between two non negative random variables which is related to a conditional version of the mean inactivity time order. A number of properties and relationship between the new notion and the concept of positive likelihood ratio dependence (PLRD) is discussed. Some results in terms of proposed notions for the Archimedean family of copulas are provided.     

Bayesian prediction of minimal repair times of a series system based on hybrid censored sample of components’ lifetimes under Rayleigh distribution

In this paper, we develop Bayesian predictive inferential procedures for prediction of repair times of a series system, applying a minimal repair strategy, using the information contained in an independent observed hybrid censored sample of the lifetimes of the components of the system, assuming the underlying distribution of the lifetimes to be Rayleigh distribution. An illustrative real data example and a simulation study are presented for the purpose of illustration and comparison of the proposed predictors.     

Simulation Modeling and Analysis: A Collateral Application and Exposition of RFID Technology

RFID technology provides in‐depth, real‐time visibility into the status of assets throughout the supply chain. However, the deployment of RFID technology may have collateral value in the high‐quality data generated by these assets. This study explores the potential value of RFID data for tactical and strategic purposes and the redesign of processes within supply chain through the deployment of simulation modeling and analysis. We present a simulation study conducted at a regional hospital for which data related to trauma patient movement was collected with an RFID‐based system. We find that not only does this data serve as the basis for successful simulation modeling, but that RFID technology may address several data‐related challenges previously identified in the simulation literature.     

The Dyadic Effects of Controlling Emotional Abuse on Subjective Emotional Experience in Iranian Older Married Couples

This randomised control trial study examines the impact of an emotional‐focused intervention on emotional abuse and subjective emotional experience in Iranian older married couples. In the study, 57 older couples were randomly assigned into control and experimental groups with the latter taking part in an intervention program over 4 weeks, twice a week, with sessions lasting 90 minutes. In the experimental group, significant improvements were found for reducing emotional abuse behaviours and increasing subjective emotional experience. Dyadic analysis revealed that a partner's ability to control their own emotional abuse significantly effects both his/her subjective emotional experience and that of their spouse.     

Meta-analysis,pretest, and shrinkage estimation of kurtosis parameters

An asymptotic theory for the improved estimation of kurtosis parameter vector is developed for multi-sample case using uncertain prior information (UPI) that several kurtosis parameters are the same. Meta-analysis is performed to obtain pooled estimator, as it is a statistical methodology for pooling quantitative evidence. Pooled estimator is a good choice when assumption of homogeneity holds but it becomes inconsistent as assumption violates, therefore pretest and Stein-type shrinkage estimators are proposed as they combine sample and nonsample information in a superior way. Asymptotic properties of suggested estimators are discussed and their risk comparisons are also mentioned.