Estimation of inverse mean: An orthogonal series approach

Authors:
Qin Wang;Xiangrong Yin
Affiliations:
Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, United States;Department of Statistics, 204 Statistics Building, The University of Georgia, Athens, GA 30602, United States
Venue:
Computational Statistics & Data Analysis
Year:
2011

Citing 2
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Abstract

In this article, we propose the use of orthogonal series to estimate the inverse mean space. Compared to the original slicing scheme, it significantly improves the estimation accuracy without losing computation efficiency, especially for the heteroscedastic models. Compared to the local smoothing approach, it is more computationally efficient. The new approach also has the advantage of robustness in selecting the tuning parameter. Permutation test is used to determine the structural dimension. Moreover, a variable selection procedure is incorporated into this new approach, which is particularly useful when the model is sparse. The efficacy of the proposed method is demonstrated through simulations and a real data analysis.