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Spatstat-pn0y.pdf
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一本关于空间统计点模式的小书。
It covers statistical methods that are currently feasible in practice and available in public
domain software. Some of these techniques are well established in the applications literature,
while some are very recent developments.
Topics covered include: statistical formulation and methodological issues; data input
and handling; R concepts such as classes and methods; nonparametric intensity estimates;
goodness-of-fit testing for Complete Spatial Randomness; maximum likelihood inference for
Poisson processes; model validation for Poisson processes; distance methods and summary
functions such as Ripley’s K function; non-Poisson point process models; simulation techniques;
fitting models using summary statistics; Gibbs point process models; fitting Gibbs
models; simulating Gibbs models; validating Gibbs models; multitype and marked point patterns;
exploratory analysis of marked point patterns; multitype Poisson process models and
maximum likelihood inference; multitype Gibbs process models and maximum pseudolikelihood;
and line segment data.
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