-
作者：HENDRICKS, W; KOENKER, R
摘要：Methods for estimating nonparametric models for conditional quantiles are suggested based on the regression quantile methods of Koenker and Bassett. Spline parameterizations of the conditional quantile functions are used. The methods are illustrated by estimating hierarchical models for household electricity demand using data from the Chicago metropolitan area. The empirical results show that lower quantiles of demand (base-load) vary only slightly across residential households. This variabili...
-
作者：SOBEL, ME; ARMINGER, G
作者单位：University of Wuppertal
摘要：This article proposes new methods for modeling household fertility decisions. Much of the demographic literature on this subject suggests that decisions relating to fertility are influenced by the orientations (attitudes, desires, intentions) of both husbands and wives, but the methods used in previous work do not indicate how wife's (husband's) orientation influences husband's (wife's) orientation, nor how these separate phenomena are combined to produce a joint decision. As such, these metho...
-
作者：HASOFER, AM; WANG, Z
摘要：A simple statistic is proposed to test the hypothesis that a sample comes from a distribution in the domain of attraction of the Gumbel distribution. It is based on the top k order statistics and is a generalization of the Shapiro-Wilk goodness-of-fit statistic. The critical region of the test and its power against the alterative that the sample comes from a distribution in another domain of attraction are studied theoretically and by simulation. The power turns out to be superior to that of o...
-
作者：BRODY, J
-
作者：HARDLE, W; HART, J; MARRON, JS; TSYBAKOV, AB
作者单位：Texas A&M University System; Texas A&M University College Station; University of North Carolina; University of North Carolina Chapel Hill; Russian Academy of Sciences
摘要：The average derivative is the expected value of the derivative of a regression function. Kernel methods have been proposed as a means of estimating this quantity. The problem of bandwidth selection for these kernel estimators is addressed here. Asymptotic representations are found for the variance and squared bias. These are compared with each other to find an insightful representation for a bandwidth optimizing terms of lower order than n-1. It is interesting that, for dimensions greater than...
