半参数和非参数模型的函数和参数估计及其在质谱数据中的应用

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2013-05-07 DOI:10.1515/ijb-2014-0066

Weiping Ma, Yang Feng, Kani Chen, Z. Ying

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引用次数: 2

摘要

摘要基于质谱数据的建模和分析，提出了一种半参数和非参数模型，该模型由用于个体位置和尺度的线性参数分量和用于公共形状的非参数回归函数组成。提出了一种同时估计参数分量和非参数函数的多步方法。在一定的正则性条件下，得到的估计量对于参数部分是一致的和渐近正态的;对于非参数部分，当带宽选择适当时，得到了最优的收敛速率。仿真结果验证了该方法的有效性和有限样本性能。该方法也适用于来自肝癌患者研究的SELDI-TOF质谱数据集。

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

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.

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.