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The characterization is an elegant property of distributions that uniquely defines the associated stochastic model. The power function distribution is a flexible model which is often used for the analysis of income data, lifetime data, and modeling of failure procedures. Weighted distributions have wide applications in real-life problems, where the probabilities of events are observed and recorded by making modifications to probabilities of actual occurrence of events considering methods of ascertainment. In this paper, we considered the weighted power function (WPF) distribution and used some reliability measures to characterize it. We characterized the WPF distribution by various techniques like mean inactivity times, mean residual function, conditional moments, conditional variance, doubly truncated mean, incomplete moments and reverse hazard function. [ABSTRACT FROM AUTHOR]

Knowledge of the load parameters of wind turbine rotor is the key problem to eliminate the misalignment of wind turbine. In this paper, an inverse method for identifying the load parameters with imprecise information is investigated by using the probability and interval variables. The interval variables are dealt with interval analysis, only the bounds of the structure response are needed avoiding time-consuming inner loop for structure response prediction. The probability variables are dealt with a proposed iteration method combined micro genetic algorithm with the search interval advance and retreat method. Firstly, at the midpoint of the interval variables, the mean of the load parameters is identified based on the proposed iteration method; secondly, the first-order Taylor expansion is employed to gain the boundary of rotor dynamic response from predicting structure response strip derived from the existence of interval variables; thirdly, the interval of the load parameters is identified via the boundary of rotor dynamic response based on the proposed iteration method. Eventually, the accuracy and efficiency of the proposed inverse method are verified by one numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we study the periodic and stable dynamics of an interactive wild and sterile mosquito population model with density-dependent survival probability. We find a release amount upper bound G ∗ , depending on the release waiting period T, such that the model has exactly two periodic solutions, with one stable and another unstable, provided that the release amount does not exceed G ∗ . A numerical example is also given to illustrate our results. [ABSTRACT FROM AUTHOR]

We present in this paper further mathematical properties of the Odd Kappa-G family of distributions. These structural properties of this family hold for any baseline model including characterizations results based on two truncated moments and hazard and reversed hazard functions. In addition, k th lower record values and extreme values of this Odd Kappa-G family are introduced. Lastly, the bivariate probability distributions of the Odd Kapp-G (BFGMOKG) family based on FGM copula are obtained. [ABSTRACT FROM AUTHOR]

The unmet need for contraception is documented as a significant determinant of unintended pregnancies and high number of induced abortions. The period immediately after an abortion is recognised as a unique opportunity to offer contraceptive services. This paper explores the association between place of abortion and women's post-abortion contraceptive behaviour. The reproductive calendar data from the National Family Health Survey (NFHS-4) (2015–16) was used for this study. Multinomial logistic regression models were used to understand factors associated with post-abortion method choices. Single decrement life-tables were built to examine rates of contraceptive discontinuation and proportional hazard models were employed to examine probability and correlates of method discontinuation. About 20% of women who underwent an abortion adopted a contraceptive method by the end of one month following an abortion. The decision to choose methods like sterilisation or intrauterine contraceptive devices (IUCDs) was associated with the place of abortion, past contraceptive behaviour, number and sex of surviving children at the time of abortion, mass media exposure, and time of the abortion. Compared to women who underwent an abortion at private health facilities, women who sought abortion at public health facilities were more likely to choose permanent methods or IUCDs. Furthermore, women who opted for an IUCD were less likely to discontinue the method compared to those using short-acting modern methods. The lack of post-abortion contraceptive choices for women is evident in the low uptake of post-abortion contraceptives in private facilities and the predominant promotion of permanent methods and IUCDs in public health facilities. [ABSTRACT FROM AUTHOR]

Demands for out-of-hours primary care (OOH-PC) services are increasing. Many citizens call because of non-urgent health problems. Nevertheless, the patients' motives for requesting medical help outside office hours remains an understudied area. This study aimed to examine motives for calling OOH-PC services in various age groups. Cross-sectional paper based questionnaire study conducted during two weeks in 2015. The OOH-PC services in two Danish regions. Randomly selected patients calling the two healthcare services and accepting to participate in the study received a questionnaire on patient characteristics, health problems, and 26 pre-defined motives based on the Andersen Behavioural Model. Multivariate regression analyses were conducted for various age groups to calculate the probability of each motive to be a significant factor for the decision to call. A total of 1,871 patients were included in the study; half were parents of children aged 0-12 years. Young adults (18 to 39 years) differed significantly from other age groups as they more often stated perceived barriers and benefits such as "Own GP no time available soon enough" and "Need for quick help because of work". Young adults more often perceive barriers and benefits, which may suggest af difference in expectations regarding the purpose of out-of-hours services and accessibility. Further research is needed to address this issue and further explore the potential gap between the citizens' expectations to the OOH-PC services and the prevailing health policies. The out-of-hours primary healthcare services are increasingly contacted for non-urgent problems, but little is known about the citizens' motives for calling. Age is associated with differences in the perceived importance of various motives for calling out-of-hours care. Young adults are more often than other age groups motivated to call due to logistical issues, such as their job. [ABSTRACT FROM AUTHOR]

Providing reliable estimates of subpopulation/area parameters has attracted increased attention due to their importance in applications such as policymaking. Due to low or even no samples from some areas, we must adopt indirect model approaches. Existing indirect small area estimation methods often assume that a single nested error regression model is suitable for all the small areas. In particular, the effects of the auxiliary variables are either fixed or have a single attraction center. In some applications, it can be more appropriate to cluster the small areas so that the effects of the auxiliary variables are fixed but have multiple centers in the nested error regression model. In this paper, we examine an extended nested error regression model in which the auxiliary variables have mixed effects with multiple centers. We use a penalty approach to identify these centers and estimate the model parameters simultaneously. We then propose two new small area mean estimators and construct estimators of their mean square errors. Simulations based on artificial and realistic finite populations show that the new estimators can be efficient. Furthermore, the confidence intervals based on the new methods have accurate coverage probabilities. We illustrate the proposed methods with the Survey of Labour and Income Dynamics conducted in Canada. [ABSTRACT FROM AUTHOR]

The correlation coefficient (CC) is a standard measure of a possible linear association between two continuous random variables. The CC plays a significant role in many scientific disciplines. For a bivariate normal distribution, there are many types of confidence intervals for the CC, such as z-transformation and maximum likelihood-based intervals. However, when the underlying bivariate distribution is unknown, the construction of confidence intervals for the CC is not well-developed. In this paper, we discuss various interval estimation methods for the CC. We propose a generalized confidence interval for the CC when the underlying bivariate distribution is a normal distribution, and two empirical likelihood-based intervals for the CC when the underlying bivariate distribution is unknown. We also conduct extensive simulation studies to compare the new intervals with existing intervals in terms of coverage probability and interval length. Finally, two real examples are used to demonstrate the application of the proposed methods. [ABSTRACT FROM AUTHOR]

As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions. [ABSTRACT FROM AUTHOR]

The scientific literature has focused on uncertainty as randomness, while limited credit has been given to what we call here the “seventh facet of uncertainty”, i.e. lack of knowledge. This paper identifies three types of lack of understanding: (i)known unknowns, which are things we know we don’t know; (ii)unknown unknowns, which are things we don’t know we don’t know; and (iii)wrong assumptions, things we think we know, but we actually don’t know. Here we discuss each of these with reference to the study of the dynamics of human–water systems, which is one of the main topics of Panta Rhei, the current scientific decade of the International Association of Hydrological Sciences (IAHS), focusing on changes in hydrology and society. In the paper, we argue that interdisciplinary studies of socio-hydrological dynamics leading to a better understanding of human–water interactions can help in coping with wrong assumptions and known unknowns. Also, being aware of the existence of unknown unknowns, and their potential capability to generate surprises or black swans, suggests the need to complement top-down approaches, based on quantitative predictions of water-related hazards, with bottom-up approaches, based on societal vulnerabilities and possibilities of failure.Editor D. Koutsoyiannis; Associate editor S. Weijs [ABSTRACT FROM AUTHOR]

In these cases getting accurate confidence intervals is of first order importance, and the methods Ignatiadis and Wager develop are likely to be useful. I want to congratulate Nikolaos Ignatiadis and Stefan Wager on a very stimulating paper on a timely and important topic. [Extracted from the article]

This article studies the problem of modeling relationship between two spherical (or directional) random variables in a regression setup. Here the predictor and the response variables are constrained to be on a unit sphere and, due to this nonlinear condition, the standard Euclidean regression models do not apply. Several past papers have studied this problem, termed spherical regression, by modeling the response variable with a von Mises-Fisher (VMF) density with the mean given by a rotation of the predictor variable. The few papers that go beyond rigid rotations are limited to one- or two-dimensional spheres. This article extends the mean transformations to a larger group—the projective linear group of transformations—on unit spheres of arbitrary dimensions, while keeping the VMF density to model the noise. It develops a Newton–Raphson algorithm on the special linear group for estimating the MLE of regression parameter and establishes its asymptotic properties when the sample-size becomes large. Through a variety of experiments, using data taken from projective shape analysis, cloud tracking, etc., and some simulations, this article demonstrates improvements in the prediction and modeling performance of the proposed framework over previously used models. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This study analyzed the factors required for a researcher to become a professor in the humanities and sociology, science and engineering, medicine and biology, and general studies fields. The study focuses on research productivity and analyzes the impact of hiatuses in research production on promotion in universities as well as the time at which such hiatuses have the least impact on promotions. I divided the factors required for promotion into three categories: academic performance (the number of published articles, books, and competitive grants and funding sources acquired), social elements (gender), and elements related to the duration of periods with no research output and their timing. The results show that the probability of promotion to professorship increases as the number of papers in Scopus, the number of books published, and the amount of acquired competitive funds increase. As expected, longer declines in research productivity reduce the probability of promotion. However, it is not always necessary for researchers to publish continuously throughout their careers; the results show that a decline in research productivity other than during the first five years and the period from 20 to 30 years after the start of the research career has no influence on academic promotions. [ABSTRACT FROM AUTHOR]

This paper deals with the steady-state behavior of an M/G/1 queue with two types of general heterogeneous service to the arriving customers and Bernoulli feedback. We first derive the steady-state probability generating functions for the queue size distributions at a random epoch as well as at a departure epoch. Next, we derive the mean queue size at random epoch and the mean waiting time. Also, we obtain the mean busy period of this model and discuss some important particular cases. [ABSTRACT FROM AUTHOR]

Simpson's paradox is a challenging topic to teach in an introductory statistics course. To motivate students to understand this paradox both intuitively and statistically, this article introduces several new ways to teach Simpson's paradox. We design a paper toss activity between instructors and students in class to engage students in the learning process. We show that Simpson's paradox widely exists in basketball statistics, and thus instructors may consider looking for Simpson's paradox in their own school basketball teams as examples to motivate students’ interest. A new probabilistic explanation of Simpson's paradox is provided, which helps foster students’ statistical understanding. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Length-Biased distributions are a special case of the more general form known as weighted distributions. We can exploit the conceptuality of Length-Biased distribution in the development of appropriate models for lifetime data. Its method is adjusting the original probability density function from real data and the expectation of those data. This modification can lead to correct conclusions of the models. Therefore, we introduced the Length-Biased version of the weighted Exponentiated inverted Weibull distribution in this paper. Various properties and the expressions for moments, coefficient of skewness, coefficient of kurtosis, moment generating function, hazard rate function, etc. are derived. The maximum likelihood estimates of the parameters of the proposed distribution are determined. The study results suggest that this distribution is an efficacious model in life time data analysis and other related fields. [ABSTRACT FROM AUTHOR]

The efficiency and safety of a new treatment is occasionally evaluated using Poisson distributed count data as the criteria in clinical studies. In such scenarios, non-inferiority tests using Poisson distributed count data are often employed and evaluated in frequentist frameworks. However, in our previous work, we proposed and presented an index that demonstrated that the Poisson parameter obtained in the Bayesian statistic framework is superior to that obtained in inference frameworks, and we also proposed a non-inferiority index of the Poisson parameters. In this paper, we expand upon these methods and propose a new index that utilizes the empirical Bayes method. The results of comparisons to approximate values in terms of accuracy as well as application to examples obtained from actual clinical studies indicate that our proposed new index is both effective and easy to understand intuitively, as it delivers probability directly. [ABSTRACT FROM AUTHOR]

In this paper, we establish a new coupled coincidence point results in partially ordered probabilistic metric spaces by utilizing the Gauge function. We use the compatibility condition between two mappings. We use monotone and mixed monotone properties of functions with respect to the ordering. Our main result has several corollaries. The main result is supported with an example which shows that the corollaries are actually contained in our main theorem. The methodology is a combination of analytic and order theoretic approaches. [ABSTRACT FROM AUTHOR]

In clinical studies, the period before an event occurs is frequently considered as an outcome. The general term used for this analytical method in clinical studies is "survival time analysis". The method employs Cox regression to compare hazards between groups. In this paper, we propose an index defined in the Bayesian framework to compare hazards. This index compares the sizes of the hazards directly. Further, we propose two methods to calculate the sizes of the hazards. One is an approximate method, by which we can easily calculate the size, and the other is an exact method. In addition, we apply the proposed index to two actual clinical studies to assess its usability. [ABSTRACT FROM AUTHOR]

In this paper, we review some results for insurance risk theory. We first introduce a variety of the insurance risk models proposed thus far. Then, we show that the expected discounted penalty function (the so-called Gerber-Shiu function) can describe some risk indicators. Next, the dividend problem is discussed; more precisely, the (approximated) optimal dividend barrier is derived and other extended dividend strategies introduced. In addition, some modified models depending on reinsurance or tax are introduced. Finally, we discuss the statistical estimation of the ruin probability and the Gerber-Shiu function. [ABSTRACT FROM AUTHOR]

There is an implicit assumption in most work that the parameters calibrated based on observations remain valid for future climatic conditions. However, this might not be true due to parameter instability. This paper investigates the uncertainty and transferability of parameters in a hydrological model under climate change. Parameter transferability is investigated with three parameter sets identified for different climatic conditions, which are: wet, intermediate and dry. A parameter set based on the baseline period (1961–1990) is also investigated for comparison. For uncertainty analysis, ak-simulation set approach is proposed instead of employing the traditional optimization method which uses a single best-fit parameter set. The results show that the parameter set from the wet sub-period performs the best when transferred into wet climate condition, while the parameter set from the baseline period is the most appropriate when transferred into dry climate condition. The largest uncertainty of simulated daily high flows for 2011–2040 is from the parameter set trained in the dry sub-period, while that of simulated daily medium and low flows lies in the parameter set from the intermediate calibration sub-period. For annual changes in the future period, the uncertainty with the parameter set from the intermediate sub-period is the largest, followed by the wet sub-period and dry sub-period. Compared with high and medium flows/runoffs, the uncertainty of low flows/runoffs is much smaller for both simulated daily flows and annual runoffs. For seasonal runoffs, the largest uncertainty is from the intermediate sub-period, while the smallest is from the dry sub-period. Apart from that, the largest uncertainty can be observed for spring runoffs and the lowest one for autumn runoffs. Compared with the traditional optimization method, thek-simulation set approach shows many more advantages, particularly being able to provide uncertainty information to decision support for watershed management under climate change. EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR not assigned [ABSTRACT FROM AUTHOR]

Flood frequency analysis (FFA) is essential for water resources management. Long flow records improve the precision of estimated quantiles; however, in some cases, sample size in one location is not sufficient to achieve a reliable estimate of the statistical parameters and thus, regional FFA is commonly used to decrease the uncertainty in the prediction. In this paper, the bias of several commonly used parameter estimators, including L-moment, probability weighted moment and maximum likelihood estimation, applied to the general extreme value (GEV) distribution is evaluated using a Monte Carlo simulation. Two bias compensation approaches: compensation based on the shape parameter, and compensation using three GEV parameters, are proposed based on the analysis and the models are then applied to streamflow records in southern Alberta. Compensation efficiency varies among estimators and between compensation approaches. The results overall suggest that compensation of the bias due to the estimator and short sample size would significantly improve the accuracy of the quantile estimation. In addition, at-site FFA is able to provide reliable estimation based on short data, when accounting for the bias in the estimator appropriately.Editor D. Koutsoyiannis; Associate editor Sheng Yue [ABSTRACT FROM PUBLISHER]

This paper proposes new confidence intervals for the weighted coefficients of variation (CV) of two-parameter exponential distributions based on the adjusted method of variance estimates recovery method (adjusted MOVER). This is then compared with the generalized confidence interval method (GCI) and the large sample method. The performance of these confidence intervals in terms of coverage probabilities and average lengths were evaluated via a Monte Carlo simulation. Simulation studies showed that the GCI should be considered as an alternative to the confidence interval estimation for the weighted CV of two-parameter exponential distributions. However, the adjusted MOVER confidence interval () can be used to estimate the weighted CV when the coefficient of variation is a positive value. The proposed confidence intervals are illustrated using a real example. [ABSTRACT FROM AUTHOR]