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利用核平滑方法估计半参数 ARCH(∞) 模型AI 解读

We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the “news impact” function. We show that the functional part of the model satisfies a type II linear integral equation and give simple conditions under which there is a unique solution. We propose an estimation method that is based…

弱条件下回代投影算法的存在性与渐近性质AI 解读

We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of popular kernel estimators provided by Mammen, Marron, Turlach and Wand and the asymptotic theory of our estimators is derived using the theory of additive projections reviewed in Bickel, Klaassen, Ritov and Wellner. Our procedure achieves the same bias and…

杂记：加性非参数回归模型的有效估计AI 解读

We define a new procedure for estimating additive nonparametric regression models. We use the integration method of Linton & Nielsen (1995) to provide starting values that are then used in a one-step backfitting procedure. We show that our new method is efficient in a certain sense and dominates the straight integration method according to mean squared error.

已知连接函数的加性回归模型估计AI 解读

We consider estimation of an additive nonparametric regression model with known link function. The asymptotic distribution of this regression estimate is given. The practical performance is investigated via an application to the study of migration between East and West Germany.

基于边际积分的结构化非参数回归核估计AI 解读

We define a simple kernel procedure based on marginal integration that estimates the relevant univariate quantity in both additive and multiplicative nonparametric regression.

密度估计的一种简单偏差缩减方法AI 解读

A new method for bias reduction in nonparametric density estimation is proposed. The method is a simple, two-stage multiplicative bias correction. Its theoretical properties are investigated, and simulations indicate its practical potential. The method is easy to compute and to analyse, and extends simply to multivariate and other estimation problems.