Estimation in Discrete Parameter Models
Estimation in Discrete Parameter Models
复制标题
离散参数模型中的估计
DOI：
10.1214/11-sts371
复制
发表时间：
2012
影响因子：
5.7
通讯作者：
Raffaello Seri
中科院分区：
文献类型：
--
作者：
C. Choirat;Raffaello Seri
在一些估计问题中，特别是在处理信息论，信号处理和生物学的应用中，理论为我们提供了额外的信息，允许我们将参数空间限制在有限数量的点。在这种情况下，我们谈论离散参数模型。即使这个问题是相当古老的，并与测试和模型选择有着有趣的联系，这些模型的渐近理论几乎从未被研究过。因此，我们讨论了一般类m-估计的相合性、渐近分布理论、信息不等式及其与有效性和超有效性的关系。
In some estimation problems, especially in applications dealing with information theory, signal processing and biology, theory provides us with additional information allowing us to restrict the parameter space to a finite number of points. In this case, we speak of discrete parameter models. Even though the problem is quite old and has interesting connections with testing and model selection, asymptotic theory for these models has hardly ever been studied. Therefore, we discuss consistency, asymptotic distribu- tion theory, information inequalities and their relations with efficiency and superefficiency for a general class of m-estimators.