FPCA-based estimation for generalized functional partially linear models

FPCA-based estimation for generalized functional partially linear models
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基于 FPCA 的广义函数部分线性模型估计

DOI:
10.1007/s00362-018-01066-8
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发表时间:
2019-01
期刊:
影响因子:
1.3
通讯作者:
Tianfa Xie
Tianfa Xie
中科院分区:
数学2区
文献类型:
--
作者:
Ruiyuan Cao;Jiang Du;Jianjun Zhou;Tianfa Xie

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在真实的数据分析中,实践者经常会遇到这样的情况:离散响应将与函数值随机变量和向量值随机变量作为预测变量相关联。本文考虑广义泛函部分线性模型(GFPLM)。GFPLM中的无穷大斜率函数由主成分基函数近似估计。然后,我们考虑了通过极大化拟似然函数得到的估计量的理论性质。分别建立了有限维参数估计的渐近正态性和无限维斜率函数估计的收敛速度。我们调查有限样本性质的估计过程中,通过Monte Carlo模拟研究和真实的数据分析。
In real data analysis, practitioners frequently come across the case that a discrete response will be related to both a function-valued random variable and a vector-value random variable as the predictor variables. In this paper, we consider the generalized functional partially linear models (GFPLM). The infinite slope function in the GFPLM is estimated by the principal component basis function approximations. Then, we consider the theoretical properties of the estimator obtained by maximizing the quasi likelihood function. The asymptotic normality of the estimator of the finite dimensional parameter and the rate of convergence of the estimator of the infinite dimensional slope function are established, respectively. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and a real data analysis.
DOI: 10.1198/tas.2003.s212
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