統計学輪講（第３４回）

日時	2000年11月21日(火)    15時00分〜16時40分
場所	経済学部5階視聴覚室
講演者  藤澤 洋徳 (東工大)
演題　	Asymptotic Properties of Conditional Maximum Likelihood Estimator
			In a Certain Exponential Model
概要   
The conditional maximum likelihood estimator is suggested 
as an alternative to the maximum likelihood estimator and 
is favorable for an estimator of a dispersion parameter 
in the normal distribution, the inverse-gaussian distribution, 
and so on. However, it is not clear whether the conditional maximum 
likelihood estimator is asymptotically efficient in general. 
Consider the case where it is asymptotically efficient and 
its asymptotic covariance depends only on an objective parameter 
in an exponential model. This remand implies that the exponential 
model possesses a certain parallel foliation. In this situation, 
this paper investigates asymptotic properties of the conditional 
maximum likelihood estimator and compares the conditional maximum 
likelihood estimator with the maximum likelihood estimator. 
We see that the bias of the former is more robust than that of 
the latter and that two estimators are very close, especially 
in the sense of bias-corrected version. 
The mean Pythagorean relation is also discussed. 

発表内容は，10月中旬に統数研で行なわれたシンポジウムにおいて
私が発表した内容とほぼ同じです．