Sample-based Maximum Likelihood Estimation of the Autologistic Model
Sample-based Maximum Likelihood Estimation of the Autologistic Model
复制标题
自逻辑模型的基于样本的最大似然估计
DOI：
10.1080/02664760701234967
复制
发表时间：
2007
影响因子：
1.5
通讯作者：
R. Reeves
中科院分区：
文献类型：
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作者：
Steen Magnussen;R. Reeves
为了快速计算格上自体模型的归一化常数，新的递归算法使得基于样本的自体参数的最大似然估计(MLE)成为可能。通过对12个模拟的420×420二元晶格的抽样，证明了…，7×7，样本量在20~600之间。将基于样本的结果与从格子上的所有二元观测得出的基准MCMC估计进行比较。平均而言，基于样本的估计有3%-7%的系统偏差，这种偏差可以通过一组校准方程减少一半以上。抽样方差的最大似然估计很大，而且通常是保守的。空间关联参数的方差约为丰度参数方差的2-10倍。估计的样本分布大多为非正态分布。我们得出的结论是，基于样本的最大似然估计和适当的样本量和估计后校准将提供完全可接受的估计。给出了预测期望抽样方差的公式。
ABSTRACT New recursive algorithms for fast computation of the normalizing constant for the autologistic model on the lattice make feasible a sample-based maximum likelihood estimation (MLE) of the autologistic parameters. We demonstrate by sampling from 12 simulated 420×420 binary lattices with square lattice plots of size 4×4, …, 7×7 and sample sizes between 20 and 600. Sample-based results are compared with ‘benchmark’ MCMC estimates derived from all binary observations on a lattice. Sample-based estimates are, on average, biased systematically by 3%–7%, a bias that can be reduced by more than half by a set of calibrating equations. MLE estimates of sampling variances are large and usually conservative. The variance of the parameter of spatial association is about 2–10 times higher than the variance of the parameter of abundance. Sample distributions of estimates were mostly non-normal. We conclude that sample-based MLE estimation of the autologistic parameters with an appropriate sample size and post-estimation calibration will furnish fully acceptable estimates. Equations for predicting the expected sampling variance are given.
《日本民俗文化第11卷》（1986年）
DOI：
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发表时间：
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期刊：
影响因子：
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作者：
通讯作者：
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DOI：
10.1109/tpami.1984.4767596
发表时间：
1984-01-01
影响因子：
23.6
作者：
GEMAN, S;GEMAN, D
通讯作者：
GEMAN, D