USING AN UNCERTAINTY MODEL TO ASSESS SALES RESPONSE TO ADVERTISING*

Implications of a probabilistic “random walk” model of incremental sales response to advertising are developed for various timing patterns of advertising expenditures. Maximum likelihood procedures for assessing advertising effectiveness and for estimating a decay (forgetting) rate are developed and applied to artificial data of known configuration and are used to assess the impact over time of a brochure program on mail-order sales. Results are also compared to those from alternative models involving various lag patterns in advertising effects.     

The effect of stress on customer perception of the frontline employee: an experimental study

Review of Managerial Science - This article examines positive effects of customer stress drawn upon the theory of excitation transfer. Contrary to previous marketing and management literature, the...     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.     

完全检查下预防维修模型建立及案例分析

为了解决维修间隔期确定的难题,建立了维修间隔期和总停机时间之间关系的目标函数,以及时间延迟维修模型.根据故障记录数据和预防维修活动的检查数据,采用最大似然法估计有关缺陷发生率和时间延迟分布等参数.在所建立目标函数和估计参数的基础上,计算出最优的维修间隔期.     

Health care futures. Part 2: The future of physician executives? Panel discussion

What is the future of health care in America? This is Part 2 of The Physician Executive panel discussion that explores the future of health care in America. To narrow this ambitious focus somewhat, the future is defined as five to 10 years hence. In Part 1, which was published in the May/June issue, Russell C. Coile, Jr., Barbara LeTourneau, MD, MBA, FACPE, James Reinertsen, MD, Uwe Reinhardt, PhD, Marshall Ruffin, MD, MPH, MBA, FACPE, and David Vogel, MS, shared their opinions about what the future holds in managed care, information technology, and biotechnology. In Part 2, Susan Cejka, Barbara LeTourneau, MD, MBA, FACPE, John Henry Pfifferling, PhD, Uwe Reinhardt, PhD, and James Todd, MD, share their views on the future of medical education and physician executives.     

Bayesian and likelihood-based inference for the bivariate normal correlation coefficient

The paper develops some objective priors for correlation coefficient of the bivariate normal distribution. The criterion used is the asymptotic matching of coverage probabilities of Bayesian credible intervals with the corresponding frequentist coverage probabilities. The paper uses various matching criteria, namely, quantile matching, highest posterior density matching, and matching via inversion of test statistics. Each matching criterion leads to a different prior for the parameter of interest. We evaluate their performance by comparing credible intervals through simulation studies. In addition, inference through several likelihood-based methods have been discussed.     

Efficient estimation in the Pareto distribution

The maximum likelihood estimation (MLE) of the probability density function (pdf) and cumulative distribution function (CDF) are derived for the Pareto distribution. It has been shown that MLEs are more efficient than uniform minimum variance unbiased estimators of pdf and CDF.