Gompertz-power series distributions

ABSTRACT

In this article, we introduce the Gompertz power series (GPS) class of distributions which is obtained by compounding Gompertz and power series distributions. This distribution contains several lifetime models such as Gompertz-geometric (GG), Gompertz-Poisson (GP), Gompertz-binomial (GB), and Gompertz-logarithmic (GL) distributions as special cases. Sub-models of the GPS distribution are studied in details. The hazard rate function of the GPS distribution can be increasing, decreasing, and bathtub-shaped. We obtain several properties of the GPS distribution such as its probability density function, and failure rate function, Shannon entropy, mean residual life function, quantiles, and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented, and simulation studies are performed for evaluation of this estimation for complete data, and the MLE of parameters for censored data. At the end, a real example is given.     

Information content of partially rank-ordered set samples

Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having more flexibility to perform the necessary judgemental ranking process. The PROS sampling has a wide range of applications in different fields ranging from environmental and ecological studies to medical research and it has been shown to be superior over ranked set sampling and simple random sampling for estimating the population mean. We study Fisher information content and uncertainty structure of the PROS samples and compare them with those of simple random sample (SRS) and ranked set sample (RSS) counterparts of the same size from the underlying population. We study uncertainty structure in terms of the Shannon entropy, Rényi entropy and Kullback–Leibler (KL) discrimination measures.     

On density and regression estimation with incomplete data

We consider the problem of estimation of a density function in the presence of incomplete data and study the Hellinger distance between our proposed estimators and the true density function. Here, the presence of incomplete data is handled by utilizing a Horvitz–Thompson-type inverse weighting approach, where the weights are the estimates of the unknown selection probabilities. We also address the problem of estimation of a regression function with incomplete data.     

Breaking Bad,Making Good: Notes on a Televisual Tourist Industry

This article explores emerging intersections between the consumption of mediated popular culture and the real and imagined topographies within which those representations are framed. Through an examination of the ‘televisual tourism’ centred around the successful TV series Breaking Bad, we scrutinise the multiple modes of sensorial and embodied travel experience enjoyed by fans of the show as they consume their way around the show’s sites, scenes and tastes in the city of Albuquerque. This exploitation of media textuality through fan tourism is, we suggest, centred upon a carefully managed commodification of crime, criminality and transgression.     

The Bayesian D-optimal design for simple beta regression model in two cases (both known and unknown nuisance parameter)

According to investigated topic in the context of optimal designs, various methods can be used to obtain optimal design, of which Bayesian method is one. In this paper, considering the model and the features of the information matrix, this method (Bayesian optimality criterion) has been used for obtaining optimal designs which due to the variation range of the model parameters, prior distributions such as Uniform, Normal and Exponential have been used and the results analysed.     

Household welfare and women empowerment through microcredit financing: Evidence from Malaysia microcredit

Microcredit financing is considered a crucial vehicle to flourish the socioeconomic conditions of women and women empowerment, especially among those who are from low-income households and deprived of access to financial services. Nevertheless, the literature arrived with a conflicting conclusion about its effect and women demonstrate that microcredit has small or no effect. This study is intended to scrutinize the impact of a productive loan provided by Amanah Ikhtiar Malaysia (AIM) on women household welfare and empowerment. A cross-sectional survey was employed through the distribution of a questionnaire to 495 of old and new borrowers. We observe that microcredit has significant positive effects on borrowers’ household income and personal asset acquisition. However, even though the effect of microcredit empowers women borrowers in households’ decisions-making, it has no effect on women control over minor financials.     

Escaping into the world of make‐up routines in Iran

Traditionally, Iranian women's use of dress and make‐up has been an arena – sometimes a battleground – for identity negotiation. The present study questions the current over‐emphasis on identity and the prevalent tendency to look for identity meanings in the use of hejab (veiling) and cosmetics. The results of fifteen interpretive in‐depth interviews with young adult women in Iran reveal that these individuals' make‐up practices are largely associated with a total immersion in the experiential, creative aspects of make‐up use and with ways to uplift their tired spirits in a monotonous environment. Make‐up routines provide these women with opportunities to escape from boredom and immerse themselves in the playful fantasies of the world of cosmetics. Despite facing various challenges, including frequent stigmatization on account of their use of make‐up, the informants in the study derive high levels of satisfaction from their make‐up practices. The study establishes that changing socio‐cultural dynamics give rise to new forms of consumption experiences in contemporary society and calls for further investigation of such experiences in women's everyday lives.     

Phase-I monitoring of general linear profiles in multistage processes

In this article, the general linear profile-monitoring problem in multistage processes is addressed. An approach based on the U statistic is first proposed to remove the effect of the cascade property in multistage processes. Then, the T² chart and a likelihood ratio test (LRT)-based scheme on the adjusted parameters are constructed for Phase-I monitoring of the parameters of general linear profiles in each stage. Using simulation experiments, the performance of the proposed methods is evaluated and compared in terms of the signal probability for both weak and strong autocorrelations, for processes with two and three stages, as well as for two sample sizes. According to the results, the effect of the cascade property is effectively removed and hence each stage can be monitored independently. In addition, the result shows that the LRT approach provides significantly better results than the T² method and outperforms it under different shift and autocorrelation scenarios. Moreover, the proposed methods perform better when larger sample sizes are used in the process. Two illustrative examples, including a real case and a simulated example, are used to show the applicability of the proposed methods.     

Spatio-temporal modeling and prediction of CO concentrations in Tehran city

One of the most important agents responsible for high pollution in Tehran is carbon monoxide. Prediction of carbon monoxide is of immense help for sustaining the inhabitants’ health level. In this paper, motivated by the statistical analysis of carbon monoxide using the empirical Bayes approach, we deal with the issue of prior specification for the model parameters. In fact, the hyperparameters (the parameters of the prior law) are estimated based on a sampling-based method which depends only on the specification of the marginal spatial and temporal correlation structures. We compare the predictive performance of this approach with the type II maximum likelihood method. Results indicate that the proposed procedure performs better for this data set.