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

Ma Weiping, Feng Yang (), Chen Kani and Ying Zhiliang
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Ma Weiping: Icahn School of Medicine at Mount Sinai, New York, NY, USA
Feng Yang: Department of Statistics, Columbia University, New York, NY, USA
Chen Kani: Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Ying Zhiliang: Department of Statistics, Columbia University, New York, NY, USA

The International Journal of Biostatistics, 2015, vol. 11, issue 2, 285-303

Abstract: 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.

Keywords: local linear regression; bandwidth selection; nonparametric estimation (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/ijb-2014-0066

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