Estimation Given Conditionals from an Exponential Family
Estimation Given Conditionals from an Exponential Family
复制标题
指数族中给定条件的估计
DOI：
--
复制
发表时间：
1994
期刊：
影响因子：
--
通讯作者：
J. Staniswalis
中科院分区：
文献类型：
--
作者：
Panagis G. Moschopoulos;J. Staniswalis
摘要 假设我们从密度为 f(x, y) 的条件指定分布中得到 n 个独立观测值 (X 1, Y 1), …, (Xn, Yn )。估计 f(x, y) 的未知参数的问题因存在难以处理的归一化常数而变得复杂，在这种条件方法中，选择该常数以使密度积分为 1。这里使用的估计方法已知当 f(x, y) 来自指数族时会产生未知参数的渐近有效估计器。它是文献中出现的一种方法的应用，由 J.K. Lindsey 提出。它非常方便地避免了对联合分布中归一化常数的依赖。参数的通常最大似然估计可以使用可用于泊松回归的软件获得。该估计方法的有用性通过指定条件为两个参数、形状和尺度、伽马而产生的模型来说明。我...
Abstract Suppose we are given n independent observations (X 1, Y 1), …, (Xn, Yn ) from a conditionally specified distribution with density f(x, y). The problem of estimating the unknown parameters of f(x, y) is complicated by the presence of an intractable normalizing constant that, in this conditional approach, is chosen so that the density integrates to 1. An approach to estimation is used here that is known to result in asymptotically efficient estimators of the unknown parameters when f(x, y) is from an exponential family. It is an application of a method that has appeared in the literature and is due to J. K. Lindsey. It very conveniently avoids the dependence on the normalizing constants in the joint distribution. The usual maximum-likelihood estimates of the parameters can be obtained using software readily available for Poisson regression. The usefulness of this estimation method is illustrated for a model resulting from specifying that the conditionals are two-parameter, shape and scale, gamma. I...