本文選題：數(shù)理統(tǒng)計(jì) + 復(fù)合分位回歸��； 參考：《中國(guó)科技論文》2017年17期

【摘要】：為了刻畫一般的可能是非高斯的連續(xù)型隨機(jī)變量間的依賴關(guān)系網(wǎng)絡(luò),研究基于復(fù)合分位回歸的方法,提出了1個(gè)稀疏的穩(wěn)健條件圖模型(robust conditional graphical model,RCGM)估計(jì),證明了該模型在維數(shù)固定時(shí)具有漸進(jìn)正態(tài)性和相合性的神諭(Oracle)性質(zhì)。通過(guò)大量模擬試驗(yàn)發(fā)現(xiàn):提出的方法具有更好的有限樣本下的表現(xiàn),在1個(gè)實(shí)際的基因型-基因組數(shù)據(jù)上的應(yīng)用也體現(xiàn)出更多的穩(wěn)健信號(hào)。
[Abstract]:In order to characterize the general dependence networks between continuous random variables, which may be non-, a sparse robust conditional graphical model is proposed based on complex quantile regression. It is proved that the model has the properties of asymptotic normality and consistency when the dimension is fixed. Through a large number of simulation experiments, it is found that the proposed method has better performance under a limited sample, and the application of the proposed method in a real genome-genome data also shows more robust signals.
【作者單位】： 中國(guó)人民大學(xué)應(yīng)用統(tǒng)計(jì)科學(xué)研究中心;中國(guó)人民大學(xué)統(tǒng)計(jì)學(xué)院;美國(guó)德州農(nóng)工大學(xué)統(tǒng)計(jì)系;
【基金】：高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金資助項(xiàng)目(20120004120007) 國(guó)家自然科學(xué)基金資助項(xiàng)目(11201479)
【分類號(hào)】：O212

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