EFFECT OF IMPRECISELY KNOWN NUISANCE PARAMETERS ON ESTIMATES OF PRIMARY PARAMETERS
EFFECT OF IMPRECISELY KNOWN NUISANCE PARAMETERS ON ESTIMATES OF PRIMARY PARAMETERS
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DOI：
10.1080/03610928908829894
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发表时间：
1989-01-01
影响因子：
0.8
通讯作者：
SPALL, JC
中科院分区：
文献类型：
--
作者：
SPALL, JC
假设所考虑的模型有两种类型的参数，α和θ，其中α表示讨厌的参数，θ表示主要参数（或感兴趣的参数）。我们将假设θ是在α固定在某个值后估计的 ，而且， 是由除用于形成θ估计值的信息以外的信息确定的 .本文研究了 在假设值中存在不确定性的情况下，特别是，我们考虑：（1）不确定性的影响， 在分散矩阵上，因此置信区间， （2）影响 的渐近性质 .本文的结果是在一个多变量的设置，并适用于广泛的一类估计，包括最大似然，最大后验概率，最小二乘。
Let the model under consideration have two types of parameters, α and θ, where α represents the nuisance parameters and θ represents the primary parameters (or parameters of interest). We will suppose that θ is estimated after α has been fixed at some value , and that is determined from information other than that used to form the estimate for θ . This paper examines the properties of in the presence of uncertainty in the assumed value for ∝. In particular, we consider: (1) the effect of the uncertainty in on the dispersion matrix, and hence confidence intervals, for ; and (2) the effect of on the asymptotic properties of . The results of this paper are developed in a multivariate setting and apply to a broad class of estimators, including maximum likelihood, maximum a posteriori, and least squares.