摘要翻译:
本文研究了Oppenheim等人2006提出的混合密度估计在随机参数AR(1)过程的聚集/解聚集问题中的渐近分布。证明了在混合密度半参数形式的温和条件下,估计量是渐近正态的。本文的证明是基于Bhansali等人2007年提出的线性随机变量二次型的极限理论。研究了聚合过程的移动平均表示。一个小型仿真研究说明了这一结果。
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英文标题:
《Asymptotic normality of the mixture density estimator in a
disaggregation scheme》
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作者:
Dmitrij Celov, Remigijus Leipus, Anne Philippe (LMJL)
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最新提交年份:
2008
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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:
The paper concerns the asymptotic distribution of the mixture density estimator, proposed by Oppenheim et al 2006, in the aggregation/disaggregation problem of random parameter AR(1) process. We prove that, under mild conditions on the (semiparametric) form of the mixture density, the estimator is asymptotically normal. The proof is based on the limit theory for the quadratic form in linear random variables developed by Bhansali et al 2007. The moving average representation of the aggregated process is investigated. A small simulation study illustrates the result.
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PDF链接:
https://arxiv.org/pdf/802.0817