Iranian obstetricians’ views about the factors that influence pregnant women's choice of delivery method: A qualitative study

BackgroundCaesarean section is a commonly performed childbirth procedure. While caesarean section is undeniably a lifesaving procedure for some mothers and babies, it can also be an unnecessary procedure for others. Different factors may be involved in the rate of birth by caesarean section.QuestionThis qualitative study was conducted in Kashan city, Iran, to explore obstetrician's views of what might influence pregnant women's choice of delivery method.MethodsObstetricians’ views and experiences were collected using semi-structured interviews. Interviews were conducted in a clinic or hospital, based on the preference of the 18 physicians who agreed to participate in the study. All interviews were recorded and transcribed. Participants were asked key questions, including: “Why do some women prefer caesarean section?”; “What kind of delivery did you have and why?”; “What kind of delivery would you recommend to pregnant women and why?” Inductive qualitative content analysis was undertaken using the method described by Morreti with 120 initial codes categorized into six categories.FindingsThe six categories developed from the initial codes were: factors relating to women, obstetricians, delivery conditions, complications, society's beliefs and the health system.ConclusionA qualitative study conducted in Iran showed that most of the factors identified by participants facilitated the choice of caesarean section. Vaginal birth was anticipated as a painful and lengthy process, with low cultural acceptance and resulting in less income for obstetricians.     

Estimation from Burr type XII distribution using progressive first-failure censored data

In this paper, a new life test plan called a progressively first-failure-censoring scheme introduced by Wu and Ku? [On estimation based on progressive first-failure-censored sampling, Comput. Statist. Data Anal. 53(10) (2009), pp. 3659–3670] is considered. Based on this type of censoring, the maximum likelihood (ML) and Bayes estimates for some survival time parameters namely reliability and hazard functions, as well as the parameters of the Burr-XII distribution are obtained. The Bayes estimators relative to both the symmetric and asymmetric loss functions are discussed. We use the conjugate prior for the one-shape parameter and discrete prior for the other parameter. Exact and approximate confidence intervals with the exact confidence region for the two-shape parameters are derived. A numerical example using the real data set is provided to illustrate the proposed estimation methods developed here. The ML and the different Bayes estimates are compared via a Monte Carlo simulation study.     

On the estimation of reliabilty function in a Weibull lifetime distribution

The estimation of the reliability function of the Weibull lifetime model is considered in the presence of uncertain prior information (not in the form of prior distribution) on the parameter of interest. This information is assumed to be available in some sort of a realistic conjecture. In this article, we focus on how to combine sample and non-sample information together in order to achieve improved estimation performance. Three classes of point estimatiors, namely, the unrestricted estimator, the shrinkage estimator and shrinkage preliminary test estimator (SPTE) are proposed. Their asymptotic biases and mean-squared errors are derived and compared. The relative dominance picture of the estimators is presented. Interestingly, the proposed SPTE dominates the unrestricted estimator in a range that is wider than that of the usual preliminary test estimator. A small-scale simulation experiment is used to examine the small sample properties of the proposed estimators. Our simulation investigations have provided strong evidence that corroborates with asymptotic theory. The suggested estimation methods are applied to a published data set to illustrate the performance of the estimators in a real-life situation.     

Exploring the effects of workplace support on work-life experience: a study of Malaysia

To date most studies of workplace support and work-life experience have been of contexts where government policies and legislation to protect employee work-life balance interests are well established, such as US, UK, and other European countries. Little scrutiny has been given to these issues in less developed economies, where support and protection in terms of work-life policies and legislation at the national level is rather limited. Malaysia, the setting of this study, provides such a context. Two types of organizational support, work flexibility and superior support, are studied for their impact on work-life experience of Malaysians. The findings of this study are based on a national survey of working adults in Malaysia. Work-life experience is conceptualized to capture conflict and enrichment aspects, as well as bidirectional effects between work and nonwork. Results show that work flexibility and superior support lower work-life conflict marginally but facilitate greater work-life enrichment among Malaysians. The paper also draws out implications of these findings for human resource development professionals operating in workplace settings within national contexts with limited mandatory work-life provisions.     

Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model

This article considers the shrinkage estimation procedure in the Cox's proportional hazards regression model when it is suspected that some of the parameters may be restricted to a subspace. We have developed the statistical properties of the shrinkage estimators including asymptotic distributional biases and risks. The shrinkage estimators have much higher relative efficiency than the classical estimator, furthermore, we consider two penalty estimators—the LASSO and adaptive LASSO—and compare their relative performance with that of the shrinkage estimators numerically. A Monte Carlo simulation experiment is conducted for different combinations of irrelevant predictors and the performance of each estimator is evaluated in terms of simulated mean squared error. Simulation study shows that the shrinkage estimators are comparable to the penalty estimators when the number of irrelevant predictors in the model is relatively large. The shrinkage and penalty methods are applied to two real data sets to illustrate the usefulness of the procedures in practice.     

Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator

The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields inefficient estimators in the presence of heteroscedasticity. It is usual practice to use estimated weighted least squares method or some adaptive methods to find efficient estimates of the regression parameters when the form of heteroscedasticity is unknown. But HCCM estimators are seldom derived from such efficient estimators for testing purposes in the available literature. The current article addresses the same concern and presents the weighted versions of HCCM estimators. Our numerical work uncovers the performance of these estimators and their finite sample properties in terms of interval estimation and null rejection rate.     

Mixed sampling plan based on exponentially weighted moving average statistic

ABSTRACT

We will design a new mixed acceptance sampling plan based on the exponentially weighted moving average statistic in this article. The plan parameters of the proposed plan are determined by an optimization problem. The efficiency of the proposed plan is compared with the existing attribute sampling plan. An industrial example is given for illustration purpose.     

Probabilistic Properties of Parametric Dual and Inverse Time Series Models Generated by ARMA Models

For the class of autoregressive-moving average (ARMA) processes, we examine the relationship between the dual and the inverse processes. It is demonstrated that the inverse process generated by a causal and invertible ARMA (p, q) process is a causal and invertible ARMA (q, p) model. Moreover, it is established that this representation is strong if and only if the generating process is Gaussian. More precisely, it is derived that the linear innovation process of the inverse process is an all-pass model. Some examples and applications to time reversibility are given to illustrate the obtained results.     

Stabilizing the Performance of Kurtosis Estimator of Multivariate Data

The estimation of the kurtosis parameter of the underlying distribution plays a central role in many statistical applications. The central theme of the article is to improve the estimation of the kurtosis parameter using a priori information. More specifically, we consider the problem of estimating kurtosis parameter of a multivariate population when some prior information regarding the the parameter is available. The rationale is that the sample estimator of the kurtosis parameter has a large estimation error. In this situation we consider shrinkage and pretest estimation methodologies and reappraise their statistical properties. The estimation based on these strategies yield relatively smaller estimation error in comparison with the sample estimator in the candidate subspace. A large sample theory of the suggested estimators are developed and compared. The results demonstrate that suggested estimators outperform the estimator based on the sample data only in the candidate subspace. In an effort to appreciate the relative behavior of the estimators in a finite sample scenario, a Monte-carlo simulation study is planned and performed. The result of simulation study strongly corroborates the asymptotic result. To illustrate the application of the estimators, some example are showcased based on recently published data.     

A note on the conditional residual lifetime of a coherent system under double monitoring

In this note, we derive some mixture representations for the reliability function of the conditional residual lifetime of a coherent system with n independent and identically distributed (i.i.d.) components under the condition that at time t₁ the jth failures has occurred and at time t₂ the kth failures (j < k) have not occurred yet. Based on the mixture representations, we then discuss the stochastic comparisons of the conditional residual lifetimes of two coherent systems with i.i.d. components.