A study on limited pre-sale strategy with consideration of consumer regret.

The effect of regret on consumers' purchasing behavior is more and more obvious. The limited pre-sale can make retailers with limited production capacity allocate two periods of stock effectively and increase their income. This paper considers the heterogeneous consumers with regret behavior in the market and constructs a model to study the retailer's optimal limited pre-sale strategy. The results show that the high price regret sensitivity negatively affects the higher price of the products in the pre-sale strategy, while the out-of-stock regret sensitivity negatively affects the retailer's profit When the production capacity is relatively low, the proportion of rational consumers is large and the high price regret sensitivity coefficient is small, the retailer should pre-sell at a limited discount and the lowest valuation, and the highest valuation is on sale, otherwise, it should be sold at a price slightly lower than the highest valuation, but when the capacity is very sufficient, the sensitive coefficient of stock-out regret is small and the proportion of rational consumers is small, the retailer should pre-sell at an unlimited premium, and a price slightly lower than the highest valuation of the pre-sale, the lowest valuation of the sale, or should be pre-sold at the highest valuation.

Racial and Ethnic Differences in Antihypertensive Medication Use and Blood Pressure Control Among US Adults With Hypertension: The National Health and Nutrition Examination Survey, 2003 to 2012.

BACKGROUND: A key to reduce and eradicate racial disparities in hypertension outcomes is to understand their causes. We aimed at evaluating racial differences in antihypertensive drug utilization patterns and blood pressure control by insurance status, age, sex, and presence of comorbidities. METHODS AND RESULTS: A total of 8796 hypertensive individuals ≥18 years of age were identified from the National Health and Nutrition Examination Survey (2003-2012) in a repeated cross-sectional study. During the study period, all 3 racial groups (whites, blacks, and Hispanics) experienced substantial increase in hypertension treatment and control. The overall treatment rates were 73.9% (95% confidence interval [CI], 71.6%-76.2%), 70.8% (95% CI, 68.6%-73.0%), and 60.7% (95% CI, 57.0%-64.3%) and hypertension control rates were 42.9% (95% CI, 40.5%-45.2%), 36.9% (95% CI, 34.7%-39.2%), and 31.2% (95% CI, 28.6%-33.9%) for whites, blacks, and Hispanics, respectively. When stratified by insurance status, blacks (odds ratio, 0.74 [95% CI, 0.64-0.86] for insured and 0.59 [95% CI, 0.36-0.94] for uninsured) and Hispanics (odds ratio, 0.74 [95% CI, 0.60-0.91] for insured and 0.58 [95% CI, 0.36-0.94] for uninsured) persistently had lower rates of hypertension control compared with whites. Racial disparities also persisted in subgroups stratified by age (≥60 and <60 years of age) and presence of comorbidities but worsened among patients <60 years of age. CONCLUSIONS: Black and Hispanic patients had poorer hypertension control compared with whites, and these differences were more pronounced in younger and uninsured patients. Although black patients received more intensive antihypertensive therapy, Hispanics were undertreated. Future studies should further explore all aspects of these disparities to improve cardiovascular outcomes.

Bayesian inference for generalized linear mixed models with predictors subject to detection limits: an approach that leverages information from auxiliary variables.

This paper is motivated from a retrospective study of the impact of vitamin D deficiency on the clinical outcomes for critically ill patients in multi-center critical care units. The primary predictors of interest, vitamin D2 and D3 levels, are censored at a known detection limit. Within the context of generalized linear mixed models, we investigate statistical methods to handle multiple censored predictors in the presence of auxiliary variables. A Bayesian joint modeling approach is proposed to fit the complex heterogeneous multi-center data, in which the data information is fully used to estimate parameters of interest. Efficient Monte Carlo Markov chain algorithms are specifically developed depending on the nature of the response. Simulation studies demonstrate the outperformance of the proposed Bayesian approach over other existing methods. An application to the data set from the vitamin D deficiency study is presented. Possible extensions of the method regarding the absence of auxiliary variables, semiparametric models, as well as the type of censoring are also discussed.

Bayesian semiparametric intensity estimation for inhomogeneous spatial point processes.

In this work we propose a fully Bayesian semiparametric method to estimate the intensity of an inhomogeneous spatial point process. The basic idea is to first convert intensity estimation into a Poisson regression setting via binning data points on a regular grid, and then model the log intensity semiparametrically using an adaptive version of Gaussian Markov random fields to smooth the corresponding counts. The inference is carried by an efficient Markov chain Monte Carlo simulation algorithm. Compared to existing methods for intensity estimation, for example, parametric modeling and kernel smoothing, the proposed estimator not only provides inference regarding the dependence of the intensity function on possible covariates, but also uses information from the data to adaptively determine the amount of smoothing at the local level. The effectiveness of using our method is demonstrated through simulation studies and an application to a rainforest dataset.