Topic
U-statistic
About: U-statistic is a research topic. Over the lifetime, 1209 publications have been published within this topic receiving 32898 citations.
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TL;DR: Methods for dealing with most data available to animal breeders, however, do not meet the usual requirements of random sampling and are likely to yield biased estimates and predictions.
Abstract: Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
1,901 citations
TL;DR: In this article, receiver operating characteristic graphs are shown to be a variant form of ordinal dominance graphs, and several different methods of constructing confidence intervals for the area measure are presented and the strengths and weaknesses of each of these methods are discussed.
1,409 citations
TL;DR: In this paper, the density-weighted average derivative of a general regression function is estimated using nonparametric kernel estimators of the density of the regressors, based on sample analogues of the product moment representation of the average derivative.
Abstract: This paper gives a solution to the problem of estimating coefficients of index models, through the estimation of the density-weighted average derivative of a general regression function. The estimators, based on sample analogues of the product moment representation of the average derivative, are constructed using nonparametric kernel estimators of the density of the regressors. Asymptotic normality is established using extensions of classical U-statistic theorems, and asymptotic bias is reduced through use of a higher-order kernel
999 citations
893 citations