Media coverage and issue visibility: State legislative responses to school bullying

Dealing with the schoolyard bully is an age-old problem; however, legislators have only recently tackled it at the state level. This study examines the adoption of anti-bullying policies from the policy diffusion and innovation perspective with an emphasis on the role of print media coverage. The study contributes to the policy diffusion literature by examining both national and local media coverage as conduits for diffusion and adds to the expanding views of the diffusion process beyond the traditional geographic proximity argument. Further, it provides the first examination of an emerging policy area important to education policy scholars. The findings show issue saliency via national media coverage drives policy adoption beyond any geographic proximity.     

Health care as a social security benefit: Institutional approaches to extending coverage

Reaching universal health‐care coverage requires an appropriate mix of compulsory contributory social insurance schemes, with mechanisms to include the informal‐economy population, and tax‐based social assistance for those whose incomes preclude their own contributions. This article urges a reversal of the trend that favours the separate development of social health insurance by separate health authorities and makes the case for the extension of health‐care coverage using existing formal‐sector social security schemes, not least because they have the necessary political backing and institutional structures. The article reviews reasons for the slow pace of coverage extension to date, and stresses the added value of incorporating health care as a social security benefit while also acknowledging the importance of retaining linkages between statutory and well‐regulated community‐based or micro health‐insurance schemes.     

Improved Prediction Intervals and Distribution Functions

Abstract.  The plug-in solution is usually not entirely adequate for computing prediction intervals, as their coverage probability may differ substantially from the nominal value. Prediction intervals with improved coverage probability can be defined by adjusting the plug-in ones, using rather complicated asymptotic procedures or suitable simulation techniques. Other approaches are based on the concept of predictive likelihood for a future random variable. The contribution of this paper is the definition of a relatively simple predictive distribution function giving improved prediction intervals. This distribution function is specified as a first-order unbiased modification of the plug-in predictive distribution function based on the constrained maximum likelihood estimator. Applications of the results to the Gaussian and the generalized extreme-value distributions are presented.     

Calibrating predictive distributions

This paper concerns prediction from the frequentist point of view. The aim is to define a well-calibrated predictive distribution giving prediction intervals, and in particular prediction limits, with coverage probability equal or close to the target nominal value. This predictive distribution can be considered in a number of situations, including discrete data and non-regular cases, and it is founded on the idea of calibrating prediction limits to control the associated coverage probability. Whenever the computation of the proposed distribution is not feasible, this can be approximated using a suitable bootstrap simulation procedure or by considering high-order asymptotic expansions, giving predictive distributions already known in the literature. Examples and applications of the results to different contexts show the wide applicability and the very good performance of the proposed predictive distribution.     

Design-based confidence limits for a distribution function

A method for constructing confidence limits for a distribution function is proposed. This method is a simple modification of the common method based on a normal approximation to the distribution of the estimated distribution function. The methods differ in how the estimated standard errors are used. The coverage properties of the two methods are compared in a simulation study. Coverage probabilities for the proposed method are found to be much closer to the nominal levels, particularly in the tails of the population distribution.     

Empirical likelihood for generalized linear models with fixed and adaptive designs

Empirical likelihood inference for generalized linear models with fixed and adaptive designs is considered. It is shown that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. Furthermore, we obtain the maximum empirical likelihood estimate of the unknown parameter and the resulting estimator is shown to be asymptotically normal. Some simulations are conducted to illustrate the proposed method.     

Fiducial and Confidence Distributions for Real Exponential Families

We develop an easy and direct way to define and compute the fiducial distribution of a real parameter for both continuous and discrete exponential families. Furthermore, such a distribution satisfies the requirements to be considered a confidence distribution. Many examples are provided for models, which, although very simple, are widely used in applications. A characterization of the families for which the fiducial distribution coincides with a Bayesian posterior is given, and the strict connection with Jeffreys prior is shown. Asymptotic expansions of fiducial distributions are obtained without any further assumptions, and again, the relationship with the objective Bayesian analysis is pointed out. Finally, using the Edgeworth expansions, we compare the coverage of the fiducial intervals with that of other common intervals, proving the good behaviour of the former.     

Performance of Interval Estimators for the Inverse Hypergeometric Distribution

The inverse hypergeometric distribution is of interest in applications of inverse sampling without replacement from a finite population where a binary observation is made on each sampling unit. Thus, sampling is performed by randomly choosing units sequentially one at a time until a specified number of one of the two types is selected for the sample. Assuming the total number of units in the population is known but the number of each type is not, we consider the problem of estimating this parameter. We use the Delta method to develop approximations for the variance of three parameter estimators. We then propose three large sample confidence intervals for the parameter. Based on these results, we selected a sampling of parameter values for the inverse hypergeometric distribution to empirically investigate performance of these estimators. We evaluate their performance in terms of expected probability of parameter coverage and confidence interval length calculated as means of possible outcomes weighted by the appropriate outcome probabilities for each parameter value considered. The unbiased estimator of the parameter is the preferred estimator relative to the maximum likelihood estimator and an estimator based on a negative binomial approximation, as evidenced by empirical estimates of closeness to the true parameter value. Confidence intervals based on the unbiased estimator tend to be shorter than the two competitors because of its relatively small variance but at a slight cost in terms of coverage probability.     

国际新闻报道的国家民族观

世界上没有绝对的新闻自由和客观报道,新闻报道服从、服务于国家民族利益,有鲜明的国家民族性。在国际传媒市场新闻霸权依然存在的今天,坚持正确的国家民族观显得尤为重要。在国际新闻报道中,必须主动出击,积极应对西方传媒对全球新闻的话语权垄断;要有大局观念,把握好度,把可读性与国家民族观有机结合起来。