Correcting On-Site Sampling Bias: A New Method with Application to Recreation Demand Analysis
针对现场抽样中频繁访客被过度代表、非访客被遗漏导致的估计偏差，提出一种分别处理内生分层和截断的实证策略，蒙特卡洛模拟和海滩休闲需求案例验证了其有效性。
Collecting data via on-site surveys is convenient and can be cost-effective. However, the on-site sampling scheme over-samples frequent site visitors and omits nonvisitors, which can result in biased and inconsistent estimation of population parameters. A common empirical approach to addressing the sampling issues is to make adjustments directly to the assumed population distribution. We propose an alternative empirical strategy that utilizes the sample distribution and treats endogenous stratification and truncation separately. Monte Carlo simulation shows this proposed empirical strategy has merit. A case study of recreation demand for coastal beaches using on-site survey data is presented. <i></i>