A new lifetime model with decreasing failure rate

In this paper, we introduce a new lifetime distribution by compounding exponential and Poisson–Lindley distributions, named the exponential Poisson–Lindley (EPL) distribution. A practical situation where the EPL distribution is most appropriate for modelling lifetime data than exponential–geometric, exponential–Poisson and exponential–logarithmic distributions is presented. We obtain the density and failure rate of the EPL distribution and properties such as mean lifetime, moments, order statistics and Rényi entropy. Furthermore, estimation by maximum likelihood and inference for large samples are discussed. The paper is motivated by two applications to real data sets and we hope that this model will be able to attract wider applicability in survival and reliability.     

Human resource development competencies as predictors of agricultural extension agents' performance in Yemen

Human resource development (HRD) issue is recognized as a significant factor for survival of organizations and professionals, including those in the sector of agricultural extension. Few empirical studies have examined the influence of HRD competency variables on agricultural extension agents' performance. The authors argue that HRD competency variables such as leadership development, communication, programme planning, programme implementation and programme evaluation could predict agricultural extension agents' performance. Questionnaire data were collected from a sample of 290 extension agents in Yemen to discover if HRD competencies affect the performance of extension agents. Regression analysis shows that three variables – competencies in programme implementation, programme evaluation and programme planning – contributed significantly to the performance of extension agents. These predictors explain 42.2% of the variance in the job performance model.     

Testing for bivariate gumbel against bivariate new better than used in expectation

It is shown that a bivariate survival function is both New Better than Used in Expectation (NBUE) and New Worse than Used in Expectation (NWUE) if and only if it is a bivariate Gumbel distribution. Statistical procedures are then presented to test whether that, within the class of bi-variate NBUE survival functions, a survival function is a Gumbel's bivariate exponential.     

A new class of probability distributions via cosine and sine functions with applications

ABSTRACT

In this paper, we introduce a new class of (probability) distributions, based on a cosine-sine transformation, obtained by compounding a baseline distribution with cosine and sine functions. Some of its properties are explored. A special focus is given to a particular cosine-sine transformation using the exponential distribution as baseline. Estimations of parameters of a particular cosine-sine exponential distribution are performed via the maximum likelihood estimation method. A simulation study investigates the performances of these estimates. Applications are given for four real data sets, showing a better fit in comparison to some existing distributions based on some goodness-of-fit tests.     

Properties of Entropies of Record Values in Reliability and Life Testing Context

In this article, the entropies of record value distributions from some continuous probability models are computed and their properties are investigated, both analytically when feasible, and numerically. In an attempt to establish relationships between entropies of the parent and the corresponding record value distributions, the entropies of record value distributions associated with the uniform, exponential, Weibull, classical Pareto, normal, gamma, beta, and Cauchy distributions are considered in this article. The entropy of record value distributions associated with the uniform, exponential, Weibull, and Pareto distributions, have tractable closed forms. The entropies of record value distributions associated with the normal, gamma, beta, and Cauchy distributions do not have tractable closed forms and need further investigations. Some general conclusions are drawn in the final section.     

Halal value creation: its role in adding value and enabling logistics service

Abstract

The need to implement halal value creation into logistics and supply chains has been recognised recently. Yet, there is a lack of efforts to leverage resources and capabilities in contributing to the halal value creation for competitive advantage and subsequently firm performance. The purpose of this paper is to understand the halal value creation of current practices and its role in adding value and enabling logistics service. A structure interview of seven third-party logistics (3PL) providers is conducted to provide more precise perception about works by viewing with the lens prescribed by practitioners rather than reviews. The results draw the novel practices of halal value creation in logistics, that have not been revealed and further identifies five emerging resources of ‘resource-based halal logistics’. These create innovation capability (ability to launch successful halal goods/services) in logistics services, which are transformed into benefits of profits for 3PLs and value for customers.     

Regression model for surrogate data in high dimensional statistics

Abstract

This paper deals with the problem of estimating the regression of a surrogated scalar response variable given a functional random one. We construct an estimator of the regression operator by using, in addition to the available (true) response data, a surrogate data. We then establish some asymptotic properties of the constructed estimator in terms of the almost-complete and the quadratic mean convergences. Notice that the obtained results generalize a part of the results obtained in the finite dimensional framework. Finally, an illustration on the applicability of our results on both simulated data and a real dataset was realized. We have thus shown the superiority of our estimator on classical estimators when we are lacking complete data.     

Multivariate analysis of parity progression—based measures of the total fertility rate and its components

This article describes a methodology for applying a discrete-time survival model—the complementary log-log model—to estimate effects of socioeconomic variables on (1) the total fertility rate and its components and (2) trends in the total fertility rate and its components. For the methodology to be applicable, the total fertility rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital fertility rate (TMFR), and the TFR itself as measures of the quantum of fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustrated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.