Gotway Carol A, Wolfinger Russell D
National Center for Environmental Health, Mailstop E70, Centers for Disease Control and Prevention, 1600 Clifton Road, NE, Atlanta, GA 30333, USA.
Stat Med. 2003 May 15;22(9):1415-32. doi: 10.1002/sim.1523.
In this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor. Prediction methods are illustrated and compared through a case study based on real data and through a detailed simulation study. The paper emphasizes a better understanding of the strengths and weaknesses of each approach.
在本文中，我们对用于分析空间计数数据的边际方法和条件方法进行了理论和实证比较。我们重点关注从广义线性混合模型框架发展而来的空间预测方法，并将它们与传统的线性（克里金法）预测器进行比较。通过基于实际数据的案例研究和详细的模拟研究对预测方法进行了说明和比较。本文强调要更好地理解每种方法的优缺点。
