Comparison of Kernel Density Estimators with Assumption on Number of Modes

A data-driven bandwidth choice for a kernel density estimator called critical bandwidth is investigated. This procedure allows the estimation to have as many modes as assumed for the density to estimate. Both Gaussian and uniform kernels are considered. For the Gaussian kernel, asymptotic results are given. For the uniform kernel, an argument against these properties is mentioned. These theoretical results are illustrated with a simulation study that compares the kernel estimators that rely on critical bandwidth with another one that uses a plug-in method to select its bandwidth. An estimator that consists in estimates of density contour clusters and takes assumptions on number of modes into account is also considered. Finally, the methodology is illustrated using environment monitoring data.     

Income distribution determinants and public spending efficiency

This paper examines the impact of public spending on income distribution in OECD countries, paying attention in particular to the impact of the quality of education and public institutions. The paper assesses the efficiency of public spending in improving income distribution by using Data Envelopment Analysis. The empirical analysis shows the existence of both input and output inefficiencies across countries when relating public social spending to the inequality in income distribution.     

Evaluation of a Seven State Criminal History Screening Pilot Program for Long-Term Care Workers

This article summarizes results from an evaluation of a federally sponsored criminal history screening (CHS) pilot program to improve screening for workers in long-term care settings. The evaluation addressed eight key issues specified through enabling legislation, including efficiency, costs, and outcomes of screening procedures. Of the 204,339 completed screenings, 3.7% were disqualified due to criminal history, and 18.8% were withdrawn prior to completion for reasons that may include relevant criminal history. Lessons learned from the pilot program experiences may inform a new national background check demonstration program.     

Monitoring the conforming fraction of high-quality processes using a control chart p under a small sample size and an alternative estimator

In this paper, we propose to change the traditional monitored statistic in a control chart p, by changing the sampling proportion ${\hat{p}}$ to a new statistics denoted as ${\tilde{p}}$ . We aim to minimize problems in designing the control chart p for high quality processes when only a small sample size is available. The idea of the new statistics is simple, as it involves taking two independent samples of a Bernoulli population. From each sample, the sampling proportion is calculated, and the new statistic to monitor is the weighted mean of the sampling proportion of each sample employed to weight the overall sampling proportion. We note that the control chart p that employs the new ${\tilde{p}}$ statistic provides more in-control values of average run length closer to the usual fixed value of 370 than the traditional statistic, that is, the sampling proportion. Numerical examples illustrate the new proposal.     

Regression models for time-varying extremes

A common approach to modelling extreme data are to consider the distribution of the exceedance value over a high threshold. This approach is based on the distribution of excess, which follows the generalized Pareto distribution (GPD) and has shown to be adequate for this type of situation. As with all data involving analysis in time, excesses above a threshold may also vary and suffer from the influence of covariates. Thus, the GPD distribution can be modelled by entering the presence of these factors. This paper presents a new model for extreme values, where GPD parameters are written on the basis of a dynamic regression model. The estimation of the model parameters is made under the Bayesian paradigm, with sampling points via MCMC. As with environmental data, behaviour data are related to other factors such as time and covariates such as latitude and distance from the sea. Simulation studies have shown the efficiency and identifiability of the model, and applying real rain data from the state of Piaui, Brazil, shows the advantage in predicting and interpreting the model against other similar models proposed in the literature.