生活在上海

我喜欢教学,也喜欢教学给我带来的感受。当看到学生们渐渐取得进步,就会心生成就感,我想这就是我对工作的满意度吧。所有这些,给我带来的,是让我觉得在上海生活的每一天都很有意思。     

Decision-Making and Evidence in Direct Practice

This article presents the results of research designed to explore decision-making by direct practitioners in selected non-profit human service settings. Forty interviews were conducted with workers at front-line, supervisory and higher-management levels. The research questions included; what sources of information are used in decision-making, what variables enhance or impede utilization of evidence-based knowledge in decision-making and what would be important resources for improving availability, access and utilization of evidence-based knowledge in decision-making? Our results indicate that respondents relied most heavily on experience; on their professional values and beliefs, and on an empathic understanding of their clients’ uniqueness. Compared to these sources of information use of research and the professional literature were less enthusiastically endorsed.     

The Social Media Release as a public relations tool: Intentions to use among B2B bloggers

The Social Media Release (SMR) is emerging as a potentially powerful public relations tool in a world of social network media, particularly when targeted at influential bloggers. The research described here studies the factors that influence bloggers to use SMRs, using the Technology Acceptance Model as a theoretical framework, concentrating specifically on their perceptions of usefulness and ease of use.     

Bias and precision of measures of survival gain from right‐censored data

In cost‐effectiveness analyses of drugs or health technologies, estimates of life years saved or quality‐adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close‐out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain – difference in median, restricted mean and extended mean survival – when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost‐effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade‐offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable. Copyright © 2015 John Wiley & Sons, Ltd.     

An experimental investigation of intrinsic motivations for giving

This paper presents results from a modified dictator experiment aimed at distinguishing and quantifying intrinsic motivations for giving. We employ an experimental design with three treatments that vary the recipient (experimenter, charity) and amount passed (fixed, varying). We find giving to the experimenter not to be significantly different from giving to a charity, when the amount the subject donates crowds out the amount donated by the experimenter such that the charity always receives a fixed amount. This result suggests that the latter treatment, first used by Crumpler and Grossman (J Public Econ 92(5–6):1011–1021, 2008), does not provide a clean test of warm glow motivation. We then propose a new method of detecting warm glow motivation based on the idea that in a random-lottery incentive (RLI) scheme, such as the one we employ, warm glow accumulates and this may lead to satiation, whereas purely altruistic motivation does not. We also provide bounds on the magnitudes of warm glow and pure altruism as motives that drive giving in our experiment.     

From Amazon to Apple: Modeling Online Retail Sales,Purchase Incidence,and Visit Behavior

In this study, we propose a multivariate stochastic model for Web site visit duration, page views, purchase incidence, and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions and involves copula components. It allows for the strong nonlinear relationships between the sales and visit variables to be explored in detail, and can be used to construct sales predictions. The model is readily estimated using maximum likelihood, making it an attractive choice in practice given the large sample sizes that are commonplace in online retail studies. We examine a number of top-ranked U.S. online retailers, and find that the visit duration and the number of pages viewed are both related to sales, but in very different ways for different products. Using Bayesian methodology, we show how the model can be extended to a finite mixture model to account for consumer heterogeneity via latent household segmentation. The model can also be adjusted to accommodate a more accurate analysis of online retailers like apple.com that sell products at a very limited number of price points. In a validation study across a range of different Web sites, we find that the purchase incidence and sales amount are both forecast more accurately using our model, when compared to regression, probit regression, a popular data-mining method, and a survival model employed previously in an online retail study. Supplementary materials for this article are available online.     

Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics

We study sequential Bayesian inference in stochastic kinetic models with latent factors. Assuming continuous observation of all the reactions, our focus is on joint inference of the unknown reaction rates and the dynamic latent states, modeled as a hidden Markov factor. Using insights from nonlinear filtering of continuous-time jump Markov processes we develop a novel sequential Monte Carlo algorithm for this purpose. Our approach applies the ideas of particle learning to minimize particle degeneracy and exploit the analytical jump Markov structure. A motivating application of our methods is modeling of seasonal infectious disease outbreaks represented through a compartmental epidemic model. We demonstrate inference in such models with several numerical illustrations and also discuss predictive analysis of epidemic countermeasures using sequential Bayes estimates.