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k 阶自回归模型中某些最小距离估计量的计算AI 解读

This article gives sufficient conditions that guarantee the existence of minimizers of special classes of functions. Using these results, a certain dass of minimum distance (MD) estimators of the autoregressive parameters is shown to exist under the additive and innovative outliers; that is, autoregression (AO and IO) models. Through simulations, a certain subclass of MD estimators is shown to perform as good as or in some cases even better than other classical estimators…

加性异常值模型中的最小距离估计AI 解读

In the additive effects outliers (A.O.) model considered here one observes $Y_{j,n} = X_j + \upsilon_{j,n}, 0 \leq j \leq n$, where $\{X_j\}$ is the first order autoregressive [AR(1)] process with the autoregressive parameter $|\rho| < 1$. The A.O.'s $\{\upsilon_{j,n}, 0 \leq j \leq n\}$ are i.i.d. with distribution function (d.f.) $(1 - \gamma_n)I\lbrack x \geq 0\rbrack + \gamma_n L_n(x), x \in \mathbb{R}, 0 \leq \gamma_n \leq 1$, where the d.f.'s $\{L_n, n \geq 0\}$ are…