摘要翻译:
研究了二阶Polya频率函数类的非参数极大似然估计。具有凹对数的密度。这是单峰密度的一个子类,总体上相当丰富。证明了NPMLE是欧氏空间中凸规划问题的解,并设计了一种类似于Jongbleod(1999)的迭代凸极小算法的算法。当真密度为PFF_2时,估计器达到Hellinger相合性。
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英文标题:
《Estimating a Polya frequency function_2》
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作者:
Jayanta Kumar Pal, Michael Woodroofe, Mary Meyer
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最新提交年份:
2007
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分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
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英文摘要:
We consider the non-parametric maximum likelihood estimation in the class of Polya frequency functions of order two, viz. the densities with a concave logarithm. This is a subclass of unimodal densities and fairly rich in general. The NPMLE is shown to be the solution to a convex programming problem in the Euclidean space and an algorithm is devised similar to the iterative convex minorant algorithm by Jongbleod (1999). The estimator achieves Hellinger consistency when the true density is a PFF_2 itself.
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PDF链接:
https://arxiv.org/pdf/708.1064