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半参数模型中 Banach 参数的有效估计AI 解读

Consider a semiparametric model with a Euclidean parameter and an infinite-dimensional parameter, to be called a Banach parameter. Assume: (a) There exists an efficient estimator of the Euclidean parameter. (b) When the value of the Euclidean parameter is known, there exists an estimator of the Banach parameter, which depends on this value and is efficient within this restricted model. Substituting the efficient estimator of the Euclidean parameter for the value of this…

时间序列模型中的自适应估计AI 解读

In a framework particularly suited for many time-series models we obtain a LAN result under quite natural and economical conditions. This enables us to construct adaptive estimators for (part of) the Euclidean parameter in these semiparametric models. Special attention is directed to group models in time series with the important subclass of models with time varying location and scale. Our set-up is confronted with the existing literature and, as examples, we reconsider…

估计量与分布的离散程度AI 解读

Arbitrary, possibly randomized estimators of a one-dimensional parameter are considered. It is shown that suitable averages of their distribution functions are more spread out than particular distribution functions, which are defined in terms of the weight functions by which the averages are taken over the parameter space and in terms of the family of distributions for the random quantity on which the estimators are based. In this way bounds are provided for the performance…

局部渐近线性估计量影响函数的一致估计AI 解读

Consider estimators which behave locally asymptotically like an average of some function taken at the observations. This function is called the influence function and one calls such estimators locally asymptotically linear. It is shown that the influence function of a locally asymptotically linear estimator can be estimated consistently and conversely, that, given a consistent estimator of the influence function, estimators can be constructed which are locally asymptotically…

位置估计量与分布的分散程度AI 解读

In the location estimation problem, translation equivariant estimators are considered. It is shown that under a mild regularity condition the distribution of such estimators is more spread out than a particular distribution which is defined in terms of the sample size and the density of the i.i.d. observations. Some consequences of this so-called spread-inequality are discussed, namely the Cramer-Rao inequality, an asymptotic minimax inequality and the efficiency of the…