Relationship building and message planning: An exploration of media challenges and strategies used during school crises at the P-12 level

Schools at the P-12 level increasingly face crises related to school shootings, violence, and other tragedies. Understanding the communication challenges schools face is paramount to assisting schools with communication before, during, and after crisis events. The current study focuses on the challenges created by media interest in such crises and the strategies implemented to overcome these challenges and provide responsible information to stakeholders. Interviews conducted with 21 school crisis teams at the P-12 level revealed that sensationalism, seeking alternative routes to information, and framing were key challenges in working with the media. In light of these challenges, administrators focused on creating media plans, building relationships with the media, being accurate but guarded, and communicating reassurance and reunification. Implications are discussed for school districts nationally.     

"第五媒体时代"的政府公共关系刍议

人类已经进入"第五媒体时代".继报纸、广播、电视、互联网之后的第五媒体抑或是手机媒体.具有媒介粘滞、瞬时精准、病毒式营销等无与伦比的传播特性.第五媒体是柄"双刃剑",它在给政府公共关系带来前所未有的挑战的同时,也带来了前所未有的机遇.利用第五媒体开拓政府公共关系的新视野,是一个政治、社会问题,也是一个技术、法律问题.     

Semiparametric inference of proportional odds model based on randomly truncated data

This paper studies the estimation in the proportional odds model based on randomly truncated data. The proposed estimators for the regression coefficients include a class of minimum distance estimators defined through weighted empirical odds function. We have investigated the asymptotic properties like the consistency and the limiting distribution of the proposed estimators under mild conditions. The finite sample properties were investigated through simulation study making comparison of some of the estimators in the class. We conclude with an illustration of our proposed method to a well-known AIDS data.     

Forward irrelevance

We investigate how to combine marginal assessments about the values that random variables assume separately into a model for the values that they assume jointly, when (i) these marginal assessments are modelled by means of coherent lower previsions and (ii) we have the additional assumption that the random variables are forward epistemically irrelevant to each other. We consider and provide arguments for two possible combinations, namely the forward irrelevant natural extension and the forward irrelevant product, and we study the relationships between them. Our treatment also uncovers an interesting connection between the behavioural theory of coherent lower previsions, and Shafer and Vovk's game-theoretic approach to probability theory.     

Characterizations based on order statistics under sampling without replacement

In this paper, we consider finite populations and investigate their characterizations by regressions of order statistics under sampling without replacement. We also investigate some asymptotic results when the size of the population goes to infinity.     

Empirical likelihood for a partially linear model with covariate data missing at random

In this paper, we consider the partial linear model with the covariables missing at random. Empirical likelihood ratios for the regression coefficients and the baseline function are investigated, the empirical log-likelihood ratios are proven to be asymptotically chi-squared and the corresponding confidence regions for the parameters of interest are then constructed. The finite sample behavior of the proposed method is evaluated with simulation and illustrated with an AIDS clinical trial dataset.     

On equality of ordinary least squares estimator,best linear unbiased estimator and best linear unbiased predictor in the general linear model

The equality of ordinary least squares estimator (OLSE), best linear unbiased estimator (BLUE) and best linear unbiased predictor (BLUP) in the general linear model with new observations is investigated through matrix rank method, some new necessary and sufficient conditions are given.     

Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time

In this article we study the problem of classification of three-level multivariate data, where multiple q$q$-variate observations are measured on u$u$-sites and over p$p$-time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.     

Characteristic function estimation of non-Gaussian Ornstein–Uhlenbeck processes

Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given their flexibility in modelling stylized features of financial series such as asymmetry, heavy tails and jumps. The use of non-Gaussian marginal distributions makes likelihood analysis of these processes unfeasible for virtually all cases of interest. This paper exploits the self-decomposability of the marginal laws of OU processes to provide explicit expressions of the characteristic function which can be applied to several models as well as to develop efficient estimation techniques based on the empirical characteristic function. Extensions to OU-based stochastic volatility models are provided.