Titlebook: Non-Regular Statistical Estimation; Masafumi Akahira,Kei Takeuchi Book 1995 Springer-Verlag New York, Inc. 1995 Estimator.Lemma.Likelihood

EXTOL

https://doi.org/10.1007/978-1-4612-2554-6Estimator; Lemma; Likelihood; Variance; distribution; form; framework; information; minimum; probability; proo

orient

Book 1995ions under consideration. In small sample and large sample theories of estimation there are well established sets of regularity conditions, and it is worth while to examine what may follow if any one of these regularity conditions fail to hold. "Non-regular estimation" literally means the theory of

簡(jiǎn)潔

Aviary

Loss of Information Associated with the Order Statistics and Related Estimators in the Case of Doubr information in a single observation and that in a statistic ., respectively. Then the value of . as . → ∞, i.e. lim. (. .) is called the loss of information associated with . and its asymptotic value as . . is called the asymptotic loss of information (see, e.g. Rao (1961)).

存心

Book 1995meaning and implications of regularity conditions, and show how the relaxation of such conditions can often lead to surprising conclusions. Their emphasis is on considering small sample results and to show how pathological examples may be considered in this broader framework.

Expiration

是貪求

Anticlimax

CAMP

仇恨

Estimation of a Common Parameter for Pooled Samples from the Uniform Distributions and the Double Ethe interval (θ — .,. ξ.) (. 1,…, .) with different nuisance parameters which is treated as a typical example in non-regular cases. In some cases the MLE and other estimators will be compared and it will be shown that the MLE based on the pooled sample is not better for both a sample of a fixed size