Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.     

Bayesian modeling of temporal properties of infectious disease in a college student population

A Bayesian statistical model is developed for analysis of the time-evolving properties of infectious disease, with a particular focus on viruses. The model employs a latent semi-Markovian state process, and the state-transition statistics are driven by three terms: (i) a general time-evolving trend of the overall population, (ii) a semi-periodic term that accounts for effects caused by the days of the week, and (iii) a regression term that relates the probability of infection to covariates (here, specifically, to the Google Flu Trends data). Computations are performed using Markov Chain Monte Carlo sampling. Results are presented using a novel data set: daily self-reported symptom scores from hundreds of Duke University undergraduate students, collected over three academic years. The illnesses associated with these students are (imperfectly) labeled using real-time (RT) polymerase chain reaction (PCR) testing for several viruses, and gene-expression data were also analyzed. The statistical analysis is performed on the daily, self-reported symptom scores, and the RT PCR and gene-expression data are employed for analysis and interpretation of the model results.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Variables Affecting Readiness to Benefit From Career Interventions

This article identifies and briefly describes the broad range of variables that may influence clients’ readiness to benefit from career interventions. The article also discusses consequences of low readiness for effective use of career interventions and addresses implications for practice as well as for future research. Variables contributing to low readiness for effective use of career interventions include personal characteristics and circumstances; knowledge of self, options, and decision making; and prior experience with career interventions. Consequences of low readiness for using career interventions include premature disengagement, negative perception of skills and interests, selective acquisition of incomplete information, premature choice foreclosure, protracted exploration, dependent decision‐making style, and poor evaluation of options.     

Computer‐Assisted Career Guidance Systems: A Part of NCDA History

The first computer‐assisted career planning systems were developed in the late 1960s and were based soundly on the best of career development and decision‐making theory. Over the years, this tradition has continued as the technology that delivers these systems’ content has improved dramatically and as they have been universally accepted as effective tools for career guidance practice. Although their widespread use in the United States is currently affected by financial conditions, the potential for using these systems in developing countries as a part of their implementation of career guidance services is promising.     

Good Effects of Bad Feelings: Negative Affectivity and Group Decision‐making

Extending the growing interest in the relationship between affect and workgroup processes, we propose that groups make better use of their distributed information and therefore make better decisions when group members are higher in negative affectivity. In an experiment, we studied the influence of negative affectivity when information was distributed among group members and when group members had fully shared information. Results indicated that negative affectivity indeed stimulates group information processing and decision quality when information is distributed among group members.     

Takacs–Fiksel Method for Stationary Marked Gibbs Point Processes

Abstract. This article studies a method to estimate the parameters governing the distribution of a stationary marked Gibbs point process. This procedure, known as the Takacs–Fiksel method, is based on the estimation of the left and right hand sides of the Georgii–Nguyen–Zessin formula and leads to a family of estimators due to the possible choices of test functions. We propose several examples illustrating the interest and flexibility of this procedure. We also provide sufficient conditions based on the model and the test functions to derive asymptotic properties (consistency and asymptotic normality) of the resulting estimator. The different assumptions are discussed for exponential family models and for a large class of test functions. A short simulation study is proposed to assess the correctness of the methodology and the asymptotic results.     

Premarital sexual permissiveness of American and Chinese college students: A cross‐cultural comparison

Employing the Reiss scale of premarital permissiveness, the author compared attitudes toward kissing, petting and coitus among American and Chinese college students. The data were composed of self‐administered questionnaires completed in class by 227 students in a state university in Texas, and by 708 students in a national university in Taiwan. As expected, Chinese students were far less permissive than American students. The pattern of less permissiveness for females than for males was preserved across the two cultures. Chinese students tended to follow the traditional code of sexual mores, while their American counterparts tended to follow the code of “permissiveness with affection.” Explanations are proffered and speculations are attempted for the Sino‐American disparities in permissiveness by selected social variables.