Factors associated with a positive childbirth experience in Brazilian women: A cross-sectional study

BackgroundHaving a positive childbirth experience is an increasingly valued outcome. Few studies evaluated the women’s satisfaction with childbirth through face-to-face interviews out of the health service environment. The objective of this study was to identify factors associated with a higher level of satisfaction with the childbirth experience among Brazilian women.MethodsThis cross-sectional study involved 287 women giving birth in two hospitals in southern Brazil. Women who gave birth to healthy newborns at term were randomly selected. Face-to-face interviews were conducted 31–37 days after delivery, at the mothers’ homes, using a structured questionnaire. Satisfaction with the childbirth experience was measured using a Likert-type scale ranging from very satisfied to very dissatisfied. Prevalence ratios (PR) were estimated using Poisson regression with robust variance.ResultsFollowing hierarchical multivariate analysis, the following factors remained associated with a higher level of satisfaction with the childbirth experience: being satisfied with antenatal care (PR = 1.30; 95% confidence interval [95%CI] = 1.06−1.59), understanding the information provided by health professionals during labor and delivery (PR = 1.40; 95%CI = 1.01−1.95), not having reported disrespect and abuse (PR = 1.53; 95%CI = 1.01−2.31), and having had the baby put to the breast within the first hour of life (PR = 1.63; 95%CI = 1.26−2.11). No association was observed with type of delivery or hospital status (public or private).ConclusionsA higher level of satisfaction with the childbirth experience is related to satisfactory antenatal care, a non-abusive, respectful, and informative environment during childbirth, and to the opportunity to breastfeed the baby within the first hour of life. In clinical practice, greater attention to these basic principles of care during pregnancy and delivery could provide more positive experiences during birth.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Progressive censoring with fixed censoring times

In this paper, we revisit the progressive Type-I censoring scheme as it has originally been introduced by Cohen [Progressively censored samples in life testing. Technometrics. 1963;5(3):327–339]. In fact, original progressive Type-I censoring proceeds as progressive Type-II censoring but with fixed censoring times instead of failure time based censoring times. Apparently, a time truncation has been added to this censoring scheme by interpreting the final censoring time as a termination time. Therefore, not much work has been done on Cohens's original progressive censoring scheme with fixed censoring times. Thus, we discuss distributional results for this scheme and establish exact distributional results in likelihood inference for exponentially distributed lifetimes. In particular, we obtain the exact distribution of the maximum likelihood estimator (MLE). Further, the stochastic monotonicity of the MLE is verified in order to construct exact confidence intervals for both the scale parameter and the reliability.     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

On MLEs of the parameters of a modified Weibull distribution based on record values

The modified Weibull distribution can be used quite effectively to model complex data from mechanical engineering or survival analysis studies that posses a monotonic or a bathtub-shape hazard rate. In this paper, we study the MLEs of the parameters of a modified Weibull distribution model in the presence of upper kth record values. The existence and uniqueness of the MLEs are proven in this case. Real data analysis is performed for illustrative purposes.     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

Confidence regions for Pareto parameters from a single and independent samples

[Abstract] Based on a single and on two independent samples, joint confidence regions for parameters of Pareto distributions are proposed with minimum volume properties and without assigning the confidence level to dimensions. In the one-sample case, comparisons are made to former simultaneous confidence sets for Pareto parameters by means of simulation and a real data set. The two-sample case is studied in various set-ups and comprises simultaneous confidence regions for the shape parameters, the scale parameters, and higher-dimensional vectors of these parameters, where common shape and common scale models are also considered.     

Tempered Mittag-Leffler Lévy processes

In this article, we introduce tempered Mittag-Leffler Lévy processes (TMLLP). TMLLP is represented as tempered stable subordinator delayed by a gamma process. Its probability density function and Lévy density are obtained in terms of infinite series and Mittag-Leffler function, respectively. Asymptotic forms of the tails and moments are given. A step-by-step procedure of the parameters estimation and simulation of sample paths is given. We also provide main results available for Mittag-Leffler Lévy processes (MLLP) and some extensions which are not available in a collective way in a single article. Our results generalize and complement the results available on Mittag-Leffler distribution and MLLP in several directions. Further, the asymptotic forms of the moments of the first-exit times of the TMLLP are also discussed.