Bootstrapping Sparsely Sampled Spatial Point Patterns

作者: Andrew R. Solow

DOI: 10.2307/1937542

关键词:

摘要: The bootstrap is a practical, nonparametric method that can be used to estimate the sampling distribution of broad class parameter estimates. We propose using samplings distance—based estimator intensity in sparsely sampled spatial point pattern. An illustration given. See full-text article at JSTOR

参考文章(4)
PETER J. DIGGLE, Robust density estimation using distance methods Biometrika. ,vol. 62, pp. 39- 48 ,(1975) , 10.1093/BIOMET/62.1.39
Karen Byth, On Robust Distance-Based Intensity Estimators Biometrics. ,vol. 38, pp. 127- ,(1982) , 10.2307/2530295
J. H. Pollard, ON DISTANCE ESTIMATORS OF DENSITY IN RANDOMLY DISTRIBUTED FORESTS Biometrics. ,vol. 27, pp. 991- ,(1971) , 10.2307/2528833