日期精度：到日 1 篇 · 到月 10 篇 · 到年 0 篇
基于选择结果抽样的定性响应模型中两种一致估计量的比较
On montre que l'estimateur de Manski-McFadden (1981) est asymptotiquement plus efficace que l'estimateur de Manski-Lerman (1977)
误差成分模型的工具变量估计
On propose des estimateurs efficaces en considerant des hypotheses alternatives sur les sources d'endogeneite et les proprietes de variance-covariance des perturbations
两阶段最小绝对离差估计量
In this paper the method of least absolute deviations is applied to the estimation of the parameters of a structural equation in the simultaneous equations model. A class of estimators called two stage least absolute deviations estimators is defined, their asymptotic properties are derived, and the problem of finding the optimal member of the class is considered. IN THIS PAPER WE APPLY the method of least absolute deviations to the estimation of the parameters of a…
最大似然与最小Logit卡方估计量的n⁻²阶均方误差
The $n^{-2}$ order mean squared errors of the maximum likelihood and the minimum chi-square estimator of the logit regression model are derived and the latter is shown to be superior for many parameter values considered. The maximum likelihood is shown to be better if the bias of each estimator is corrected to the order of $n^{-1}$; however, the difference is shown to be negligibly small in many practical situations.
一般定性响应模型的最大似然、最小卡方与非线性加权最小二乘估计
This article deals with the general qualitative response model involving several polytomous variables. An extension of Berkson's minimum chi-square estimator to this model is defined and shown to be not always asymptotically efficient, and the conditions under which it is so are derived. It is also shown that the nonlinear weighted least squares method proposed by Walker and Duncan [11] is equivalent to the method of scoring.
双变量 Probit 分析：最小卡方方法
In this article we propose two minimum chi-square estimators for a bivariate probit model. We call one estimator the Full Information and the other Limited Information Minimum Chi-Square because the first takes account of all the a priori information while the second does not. Both estimators are shown to be consistent. Moreover, the first is shown to be asymptotically as efficient as the maximum likelihood estimator and yet is computationally much simpler. For illustration,…