摘要翻译:
本文考虑了相依数据的非参数估计,其中观测数据不一定来自线性过程。利用2-混合相关测度研究了密度估计,并讨论了非参数回归中的相关问题。我们将2-混合下的结果与假设过程为线性的结果进行了比较。在面板时间序列的背景下,当一个人观察来自几个人的数据时,假设过程的联合线性往往太强了。相反,本文所开发的方法使我们能够通过2-混合来量化依赖关系,这允许非线性。我们提出了面板均值函数的一个估计,并得到了它的收敛速度。我们证明了在一定的条件下,允许面板中的个体数随时间增加可以提高收敛速度。
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英文标题:
《Nonparametric estimation for dependent data with an application to panel
time series》
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作者:
Jan Johannes and Suhasini Subba Rao
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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:
In this paper we consider nonparametric estimation for dependent data, where the observations do not necessarily come from a linear process. We study density estimation and also discuss associated problems in nonparametric regression using the 2-mixing dependence measure. We compare the results under 2-mixing with those derived under the assumption that the process is linear. In the context of panel time series where one observes data from several individuals, it is often too strong to assume the joint linearity of processes. Instead the methods developed in this paper enable us to quantify the dependence through 2-mixing which allows for nonlinearity. We propose an estimator of the panel mean function and obtain its rate of convergence. We show that under certain conditions the rate of convergence can be improved by allowing the number of individuals in the panel to increase with time.
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PDF链接:
https://arxiv.org/pdf/706.3923