论文标题
指数半多图模型的参数不确定性估计：两种具有基于校验和分位数过滤的广义bootstrap方法
Parameter uncertainty estimation for exponential semi-variogram models: Two generalized bootstrap methods with check- and quantile-based filtering
论文作者
论文摘要
鉴于将参数模型拟合到空间相关的数据所需的两步过程，对半比较图模型的参数标准误差的估计是具有挑战性的。在社会流行病学中的应用中，我们专注于适合500至2000年观测数据之间的数据的指数半多相图模型，并且对采样设计几乎没有控制。以前提出的用于估计标准错误的方法不能在这种情况下应用。使用通用最小二乘在记忆能力方面，近似封闭形式的解决方案过于昂贵。 OLEA和Pardo-Igúzquiza提出的广泛的引导程序仍然适用于加权而不是广义的最小二乘。但是，标准误差估计值极为偏见和不精确。因此，我们提出了一种添加到广义引导程序中的过滤方法。通过一项模拟研究对新的开发进行了评估，该研究表明，与基于分位数的滤波器方法和先前开发的方法相比，具有基于检查的过滤的广义引导带可大大改善结果。我们使用出生体重数据提供案例研究。
The estimation of parameter standard errors for semi-variogram models is challenging, given the two-step process required to fit a parametric model to spatially correlated data. Motivated by an application in the social-epidemiology, we focus on exponential semi-variogram models fitted to data between 500 to 2000 observations and little control over the sampling design. Previously proposed methods for the estimation of standard errors cannot be applied in this context. Approximate closed form solutions are too costly using generalized least squares in terms of memory capacities. The generalized bootstrap proposed by Olea and Pardo-Igúzquiza is nonetheless applicable with weighted instead of generalized least squares. However, the standard error estimates are hugely biased and imprecise. Therefore, we propose a filtering method added to the generalized bootstrap. The new development is presented and evaluated with a simulation study which shows that the generalized bootstrap with check-based filtering leads to massively improved results compared to the quantile-based filter method and previously developed approaches. We provide a case study using birthweight data.
