Subjective experiences of stress workload, and bodily discomfort as a function of age and type of computer work

An increasingly older workforce and a growing emphasis on jobs involving interaction with computers have resulted in a need to more carefully examine the relationship between ageing and computer-based work. In this study, a sample of 394 subjects ranging in age from 20-75 years performed a computer task across a 3-day period. Three different types of computer-based jobs (data entry, information retrieval, and accounts balancing) performed at three large US companies were simulated. Age differences in the subjective experience of stress, workload, and bodily discomfort were evaluated. The results indicated that age effects for these measures varied according to task. The older subjects perceived greater workload for the more mentally challenging problem-solving oriented accounts balancing task (Which involved a graphical user interface) than the younger participants, even with increased exposure to the task. However, the older subjects generally experienced less stress than the younger subjects on an information retrieval task that involved a more socially interactive telephone component. A positive relationship between the frustration component of workload and the measure of stress was also found, suggesting an important link between the constructs of stress and workload. Overall, the outcomes of this study provide important insights into design interventions intended to accommodate older as well as younger persons in the workforce.     

On proportional odds models

Recently, Marshall and Olkin (Biometrika 84(3):641–652 1997) introduced a family of distributions by adding a new parameter to a survival function. In this paper, we give physical interpretation of the family using odds function. It is shown that the family of distributions satisfies the property of proportional odds function. We, then, develop a generalized family and study its properties. Further, we give various definitions of proportional odds model in the bivariate set up. Based on these, we introduce new families of bivariate distributions and study their properties.     

Shared frailty model for recurrent event data with multiple causes

The topic of heterogeneity in the analysis of recurrent event data has received considerable attention recent times. Frailty models are widely employed in such situations as they allow us to model the heterogeneity through common random effect. In this paper, we introduce a shared frailty model for gap time distributions of recurrent events with multiple causes. The parameters of the model are estimated using EM algorithm. An extensive simulation study is used to assess the performance of the method. Finally, we apply the proposed model to a real-life data.     

Nonparametric estimation of mean residual quantile function under right censoring

In this paper, we develop non-parametric estimation of the mean residual quantile function based on right-censored data. Two non-parametric estimators, one based on the empirical quantile function and the other using the kernel smoothing method, are proposed. Asymptotic properties of the estimators are discussed. Monte Carlo simulation studies are conducted to compare the two estimators. The method is illustrated with the aid of two real data sets.     

Proportional cause-specific reversed hazards model

The proportional reversed hazards model explains the multiplicative effect of covariates on the baseline reversed hazard rate function of lifetimes. In the present study, we introduce a proportional cause-specific reversed hazards model. The proposed regression model facilitates the analysis of failure time data with multiple causes of failure under left censoring. We estimate the regression parameters using a partial likelihood approach. We provide Breslow's type estimators for the cumulative cause-specific reversed hazard rate functions. Asymptotic properties of the estimators are discussed. Simulation studies are conducted to assess their performance. We illustrate the applicability of the proposed model using a real data set.     

An investigation of Bayesian inference approach to model validation with non-normal data

Quantitative model validation is playing an increasingly important role in performance and reliability assessment of a complex system whenever computer modelling and simulation are involved. The foci of this paper are to pursue a Bayesian probabilistic approach to quantitative model validation with non-normality data, considering data uncertainty and to investigate the impact of normality assumption on validation accuracy. The Box–Cox transformation method is employed to convert the non-normality data, with the purpose of facilitating the overall validation assessment of computational models with higher accuracy. Explicit expressions for the interval hypothesis testing-based Bayes factor are derived for the transformed data in the context of univariate and multivariate cases. Bayesian confidence measure is presented based on the Bayes factor metric. A generalized procedure is proposed to implement the proposed probabilistic methodology for model validation of complicated systems. Classic hypothesis testing method is employed to conduct a comparison study. The impact of data normality assumption and decision threshold variation on model assessment accuracy is investigated by using both classical and Bayesian approaches. The proposed methodology and procedure are demonstrated with a univariate stochastic damage accumulation model, a multivariate heat conduction problem and a multivariate dynamic system.     

Quantile based reliability aspects of partial moments

Partial moments are extensively used in literature for modeling and analysis of lifetime data. In this paper, we study properties of partial moments using quantile functions. The quantile based measure determines the underlying distribution uniquely. We then characterize certain lifetime quantile function models. The proposed measure provides alternate definitions for ageing criteria. Finally, we explore the utility of the measure to compare the characteristics of two lifetime distributions.     

Comparison of cumulative incidence functions of current status competing risks data with discrete observation times

Abstract

Competing risks data with current status censoring arise frequently from transversal studies in demography, epidemiology and reliability theory; where the only information about lifetime is whether the event of interest has occurred or not before a monitoring time. In practice, the monitoring times are discrete, but most of the studies consider them as continuous in nature. In the present paper, we propose a non parametric test for comparing cumulative incidence functions of current status competing risks data while the observation (monitoring) times are discrete. Asymptotic distribution of the test statistic is also derived. A simulation study is conducted to assess the finite sample behavior of the test statistic. The practical utility of the procedure is well demonstrated using a real-life data set on menopausal history of 2423 women given in Jewell, van der Laan, and Henneman (2003 Jewell, N. P., M. van der Laan, and T. Henneman. 2003. Nonparametric estimation from current status data with competing risks. Biometrika 90 (1):183–197. doi: 10.1093/biomet/90.1.183.[Crossref], [Web of Science ®] , [Google Scholar]).     

A generalized Pearson system useful in reliability analysis

In the present note, we study an extended class of Pearson system of distributions in the context of reliability. It is shown that the proposed class of models can be characterized by a relatioaship between the failure rate and the conditional moments. Further, we develop a procedure to identify an increasing (decreasing) failure rate model in the generalized Pearson system.