We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

中文翻译：

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重尾和非正态数据的参数和半参数回归方法的准蒙特卡罗比较：医疗成本的应用。

我们对医疗费用的参数和半参数回归方法的最新发展进行了准蒙特卡洛比较，两者之间相互对照，也与标准做法相抵触。将2007-2008财政年度英国国家卫生局医院住院患者的总人数（每位患者总和）随机分为两个大小相等的亚人群，以形成一个估计集和一个验证集。使用验证集评估样本外，条件密度近似估计值在预测条件均值方面显示出可观的前景，在预测准确性方面表现最佳，在偏倚和拟合优度方面表现最好的四个。偏差的最佳表现模型是具有平方根变换的因变量的线性回归，而具有平方根链接函数和泊松分布的广义线性模型在拟合优度方面表现最佳。与我们在比较中考虑的其他模型相比，利用日志链接的常用模型显示出较差的性能。