Premium
ERROR VARIANCE ESTIMATION FOR THE SINGLE‐INDEX MODEL
Author(s) -
Kulasekera K. B.,
Lin Wei
Publication year - 2010
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2010.00575.x
Subject(s) - estimator , mathematics , statistics , delta method , variance (accounting) , dimension (graph theory) , index (typography) , statistical inference , asymptotic distribution , single index model , asymptotic analysis , econometrics , computer science , accounting , world wide web , pure mathematics , business
Summary Single‐index models provide one way of reducing the dimension in regression analysis. The statistical literature has focused mainly on estimating the index coefficients, the mean function, and their asymptotic properties. For accurate statistical inference it is equally important to estimate the error variance of these models. We examine two estimators of the error variance in a single‐index model and compare them with a few competing estimators with respect to their corresponding asymptotic properties. Using a simulation study, we evaluate the finite‐sample performance of our estimators against their competitors.