论文检索

摘要暂缺

摘要暂缺

单指标模型中的最优平滑AI 解读

Single-index models generalize linear regression. They have applications to a variety of fields, such as discrete choice analysis in econometrics and dose response models in biometrics, where high-dimensional regression models are often employed. Single-index models are similar to the first step of projection pursuit regression, a dimension-reduction method. In both cases the orientation vector can be estimated root-n consistently, even if the unknown univariate function (or…

需求法则的经验证据AI 解读

For market demand to satisfy the law of demand, it is sufficient that the mean of all households' income effect matrices is positive definite. The authors show how this mean income effect matrix can be estimated from cross section data under metonymy, an assumption about the distribution of household characteristics. The estimation uses the nonparametric method of average derivatives. When the method is applied to U.K. family expenditure data, the estimated mean income…

摘要暂缺

摘要暂缺

摘要暂缺

非参数回归函数估计中的最优带宽选择AI 解读

Kernel estimators of an unknown multivariate regression function are investigated. A bandwidth-selection rule is considered, which can be formulated in terms of cross validation. Under mild assumptions on the kernel and the unknown regression function, it is seen that this rule is asymptotically optimal.

非参数回归函数估计量的重对数律AI 解读

We study the estimation of a regression function by two classes of estimators, the Nadaraya-Watson Kernel type estimators and the orthogonal polynomial estimators. We obtain sharp pointwise rates of strong consistency by establishing laws of the iterated logarithm for the two classes of estimators. These results parallel those of Hall (1981) on density estimation and extend those of Noda (1976) on strong consistency of kernel regression estimators.