Testing predictor contributions in sufficient dimension reduction
Copyright policy )Testing predictor contributions in sufficient dimension reduction
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required. The general approach is based on sufficient dimension reduction, the idea being to replace the predictor vector with a lower-dimensional version without loss of information on the regression. Methodology using sliced inverse regression is developed in detail.
- University of Minnesota United States
- University of Minnesota Morris United States
- University of Minnesota System United States
Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Central subspace, Statistics Theory (math.ST), nonparametric regression, 62G08, 62G09, 62H05, central subspace, FOS: Mathematics, Nonparametric regression and quantile regression, sliced inverse regression, Characterization and structure theory for multivariate probability distributions; copulas, 62G08 (Primary) 62G09,62H05. (Secondary)
Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Central subspace, Statistics Theory (math.ST), nonparametric regression, 62G08, 62G09, 62H05, central subspace, FOS: Mathematics, Nonparametric regression and quantile regression, sliced inverse regression, Characterization and structure theory for multivariate probability distributions; copulas, 62G08 (Primary) 62G09,62H05. (Secondary)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).111 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!