Premium
A Composite Likelihood Cross‐validation Approach in Selecting Bandwidth for the Estimation of the Pair Correlation Function
Author(s) -
GUAN YONGTAO
Publication year - 2007
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2006.00533.x
Subject(s) - smoothing , mathematics , kernel smoother , bandwidth (computing) , nonparametric regression , cross validation , nonparametric statistics , kernel (algebra) , kernel density estimation , algorithm , statistics , function (biology) , regression function , correlation , mathematical optimization , kernel method , artificial intelligence , computer science , regression , discrete mathematics , geometry , radial basis function kernel , computer network , estimator , evolutionary biology , support vector machine , biology
Abstract. A useful tool while analysing spatial point patterns is the pair correlation function (e.g. Fractals, Random Shapes and Point Fields, Wiley, New York, 1994). In practice, this function is often estimated by some nonparametric procedure such as kernel smoothing, where the smoothing parameter (i.e. bandwidth) is often determined arbitrarily. In this article, a data‐driven method for the selection of the bandwidth is proposed. The efficacy of the proposed approach is studied through both simulations and an application to a forest data example.