Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/75684

Functional and Parametric Estimation in a Semi-and Nonparametric Model with Application to Mass-Spectrometry Data

Authors Ma, Weiping
Feng, Yang
Chen, Kani View this author's profile
Ying, Zhiliang
Issue Date 2015
Source International Journal of Biostatistics , v. 11, (2), November 2015, p. 285-303
Summary Motivated by modeling and analysis of mass-spectrometry data, a semi-and nonparametric model is proposed that consists of linear parametric components for individual location and scale and a nonparametric regression function for the common shape. A multi-step approach is developed that simultaneously estimates the parametric components and the nonparametric function. Under certain regularity conditions, it is shown that the resulting estimators is consistent and asymptotic normal for the parametric part and achieve the optimal rate of convergence for the nonparametric part when the bandwidth is suitably chosen. Simulation results are presented to demonstrate the effectiveness and finite-sample performance of the method. The method is also applied to a SELDI-TOF mass spectrometry data set from a study of liver cancer patients. © 2015 by De Gruyter.
Subjects
ISSN 1557-4679
Language English
Format Article
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