Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency re...Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency relationship of the response variable with respect to independent variables.This paper proposes an automatic structure identification and variable selection procedure for semiparametric spatial autoregressive model,based on the generalized method of moments and the smooth-threshold estimating equations.The novel method is easily implemented without solving any convex optimization problems.Model identification consistency is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the varying ones with probability approaching to one.Detailed issues on computation and turning parameter selection are discussed.Some Monte Carlo simulations are conducted to demonstrate the finite sample performance of the proposed procedure.Two empirical applications on Boston housing price data and New York leukemia data are further considered.展开更多
常规降噪方法在应用于时域航空电磁信号降噪时需根据噪声情况人为进行参数调整,自适应性较差。总体经验模态分解(EEMD)算法对非线性、非平稳信号处理具有良好的自适应特性,传统的EEMD算法进行噪声抑制是将高频本征模态分量滤除,将低频...常规降噪方法在应用于时域航空电磁信号降噪时需根据噪声情况人为进行参数调整,自适应性较差。总体经验模态分解(EEMD)算法对非线性、非平稳信号处理具有良好的自适应特性,传统的EEMD算法进行噪声抑制是将高频本征模态分量滤除,将低频分量重构得到降噪信号,这种方法易失掉高频分量中的有效信号。本文提出一种改进的EEMD降噪算法,应用于时域航空电磁信号的处理。该方法结合时域航空电磁信号的衰减特性,将信号EEMD分解后得到本征模态分量,其中包含信号和噪声,经Savitzky-Golay平滑滤波,再将高频部分进行阈值去噪,最后得到干净的本征模态分量进行重构。实验结果表明在输入信号信噪比小于等于15 d B的情况下,输出信噪比能够提高12 d B左右,在抑制噪声的同时保留了更多有效信息。展开更多
基金supported by the Natural Science Foundation of Hunan Province(Grant 2022JJ30368)the National Natural Science Foundation of China(Grants 11801168,11801169,12071124)the Discovery Grants(RG/PIN261567-2013)from National Science and Engineering Council of Canada.
文摘Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency relationship of the response variable with respect to independent variables.This paper proposes an automatic structure identification and variable selection procedure for semiparametric spatial autoregressive model,based on the generalized method of moments and the smooth-threshold estimating equations.The novel method is easily implemented without solving any convex optimization problems.Model identification consistency is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the varying ones with probability approaching to one.Detailed issues on computation and turning parameter selection are discussed.Some Monte Carlo simulations are conducted to demonstrate the finite sample performance of the proposed procedure.Two empirical applications on Boston housing price data and New York leukemia data are further considered.
文摘常规降噪方法在应用于时域航空电磁信号降噪时需根据噪声情况人为进行参数调整,自适应性较差。总体经验模态分解(EEMD)算法对非线性、非平稳信号处理具有良好的自适应特性,传统的EEMD算法进行噪声抑制是将高频本征模态分量滤除,将低频分量重构得到降噪信号,这种方法易失掉高频分量中的有效信号。本文提出一种改进的EEMD降噪算法,应用于时域航空电磁信号的处理。该方法结合时域航空电磁信号的衰减特性,将信号EEMD分解后得到本征模态分量,其中包含信号和噪声,经Savitzky-Golay平滑滤波,再将高频部分进行阈值去噪,最后得到干净的本征模态分量进行重构。实验结果表明在输入信号信噪比小于等于15 d B的情况下,输出信噪比能够提高12 d B左右,在抑制噪声的同时保留了更多有效信息。
