A pseudo-complete sample technique for estimation from censored samples

An iterative procedure is presented whereby singly right censored samples are transformed into pseudo-complete samples. This involves the use of order statistics to calculate expected “complete” values of the censored observations. Estimates of the distribution parameters are then calculated by employing standard complete sample estimators. This procedure is applicable to various types of distributions.     

Distribution of Increases in Residual Log Likelihood for Nested Spatial Models

Here we review nested relationships between models in the Matérn family of spatial models. The problem of comparing nested statistical models is straightforward in regular parametric problems via the likelihood ratio statistics and its asymptotic distribution. Here we examine the distribution of increments in residual log likelihood between nested spatial models when the null hypothesis is that the spatial structure is a convex combination of white noise and the de Wijs process, also known by its logarithmic covariance function. This study is carried out by simulation of spatial processes and the important aspects of this work include how to simulate a spatial process of order 0, the lack of strong bias in the estimates of variance components, and the validity of the usual asymptotic results for nested spatial models examined here.     

Regression Tolerance Intervals

In this article, we discuss the utility of tolerance intervals for various regression models. We begin with a discussion of tolerance intervals for linear and nonlinear regression models. We then introduce a novel method for constructing nonparametric regression tolerance intervals by extending the well-established procedure for univariate data. Simulation results and application to real datasets are presented to help visualize regression tolerance intervals and to demonstrate that the methods we discuss have coverage probabilities very close to the specified nominal confidence level.     

Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation

High surgical bed occupancy levels often result in heightened staff stress, frequent surgical cancellations, and long surgical wait times. This congestion is in part attributable to surgical scheduling practices, which often focus on the efficient use of operating rooms but ignore resulting downstream bed utilization. This paper describes a transparent and portable approach to improve scheduling practices, which combines a Monte Carlo simulation model and a mixed integer programming (MIP) model. For a specified surgical schedule, the simulation samples from historical case records and predicts bed requirements assuming no resource constraints. The MIP model complements the simulation model by scheduling both surgeon blocks and patient types to reduce peak bed occupancies. Scheduling guidelines were developed from the optimized schedules to provide surgical planners with a simple and implementable alternative to the MIP model. This approach has been tested and delivered to planners in a health authority in British Columbia, Canada. The models have been used to propose new surgical schedules and to evaluate the impact of proposed system changes on ward congestion.     

Intimate partner violence and the spatial pattern of maternal healthcare services utilization among parous married women in northern Nigeria

Journal of Population Research - The low uptake of maternal healthcare services (MHS) in Nigeria is implicated in the country’s poor maternal health outcomes. In northern Nigeria where these...     

Multiple Rhythmically Varying Prices,the Timing of Factor Demands and Induced Technological Change

ABSTRACT: This Paper the existing literature on optimal capital utilisation and shiftwork patterns by arguing that labour is not the only input to exhibit a price which varies over the time of day week or year Indeed in certain situations as in the w e of capital intensive industries which are heavy consumers of electricity labour costs may not even be the most important consideration The introduction of a second varying input price can completely alter the optimal operating times of production units Once this extension has been adopted it is a fairly easy final step to demonstrate that some firms face a strong inducement to develop technologies which allow them to use their capital and labour independently This type of technological change has become commonplace in recent years but largely unexplained by the existing literature These developments have important implications for the organisation of work patterns and the flexible use of the labour input     

AN EMPIRICAL EVALUATION OF CONSTANT AND ADAPTIVE COMPUTER FORECASTING MODELS FOR INVENTORY CONTROL

This study compares the performance of one constant model with two adaptive models. MAD and bias were used as criteria for evaluating performance. The results of testing the adaptive models with the constant model were contrary to the expected better performance of the adaptive models; no significant differences were found between the models over known horizontal, trend, and seasonal demand patterns. However, some of the practical advantages of adaptive forecasting models in business suggest their continued use.