current status data
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Purpose The purpose of this study is to optimize the location of hospitals in Gorgan, Iran, to provide desirable services to citizens in the event of an earthquake crisis. Design/methodology/approach This paper, due to target, is practical and developmental, due to doing method is descriptive and analytical and due to information gathering method is documental and surveying. In the present study, the capabilities of genetic algorithms and imperialist competition algorithm in MATLAB environment in combination with GIS capabilities have been used. In fact, cases such as route blocking, network analysis and vulnerability raster have been obtained from GIS-based on current status data, and then the output of this information is entered as non-random heuristic information into genetic algorithms and imperialist competition algorithm in MATLAB environment. Findings After spatial optimization, the hospital service process has become more favorable. Also, the average cost and transfer vector from hospitals to citizens has decreased significantly. By establishing hospitals in the proposed locations, a larger population of citizens can access relief services in less time. Originality/value Spatial optimization of relief centers, including hospitals, is one of the issues that can be of significant importance, especially in the event of an earthquake crisis. The findings of the present study and the originality, efficiency and innovation of the used methods can provide a favorable theoretical framework for the success of earthquake crisis management projects.

In this paper, we proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters. The reasonability of the proposed method is verified by the well performance presented in the extensive simulation study. We also analyzed a real data set as illustration.

We use a historical data about breathlessness in British coal miners to compare two methods centered around a differential equation for deriving the age-specific incidence from aggregated current status data with age information, i.e. age-specific prevalence data. Special focus is put on estimating confidence bounds. For this, we derive a maximum likelihood (ML) estimator for estimating the age-specific incidence from the prevalence data and confidence bounds are calculated based on classical ML theory. Second, we construct a Markov-Chain-Monte-Carlo (MCMC) algorithm to estimate confidence bounds, which implements a weighted version of the differential equation into the prior of the MCMC algorithm. The confidence bounds for both methods are compared and it turns out that the MCMC estimates approach the ML estimates if the prior gives strong weight to the differential equation.

Background: Duration of breastfeeding is an important health indicator of mother and child. There are various indirect epidemiological methods available to estimate the duration of breastfeeding from cross sectional data. Objective: To estimate the distribution of duration of breastfeeding at national level cross sectional data and compare various available technique. The impact of the sampling frame (ascertain of the individual understudy) is also evaluated. Method: National Family Health Survey (NFHS-IV) data is used. Duration of breastfeeding of only those children who were born before 60 months from survey date were included in the study. The technique of Current Status Data, Life Table Analysis, and Kaplan Meier (KM) estimator is applied to assess the distribution of duration of breastfeeding. Result: The mean estimate is 32.84, 33.14 and 33.64 months by Kaplan Maier Estimator, Current Status Data and Life Table Analysis respectively. The Current Status and Life Table method are better than Kaplan Meier Estimator as it is doesn’t based on recall data and heaping present in the data. Conclusion: One must be very cautions while estimating the various epidemiological parameters from cross section data set. The assumptions of the methodology as per data available should be evaluate. If such data is not available, the available methodology may be modified. Regression analysis based on Current Status data technique may be used to assess the impact of various clinical and epidemiological factors (such as nutrition of mother, health status of mother etc.) on duration of breastfeeding

A Penalized Maximum Likelihood Estimation (PMLE) procedure is proposed for Cox proportional hazards frailty model with noninformative bivariate current status data. An integrated splines (I-splines) was used to approximate the two unknown baseline cumulative hazard functions of the failure times. The one-parameter gamma frailty distribution was used to model the correlation between the two failure times. An easy to implement computational algorithm is proposed to estimate the regression and splines parameters. Bayesian technique as proposed by Wahba (1983) was employed for the variance estimation. The statistical properties of the estimated parameters were studied through extensive simulation and it was observed that the PMLEs were consistent, asymptotically normal and efcient. In addition, the estimators were robust to the choice of knots, censoring rates and type of frailty distribution used. The proposed methodology is further demonstrated through the analysis of the tumorigenicity experiment data by Lindsey and Ryan (1994).