Envelopes for censored quantile regression
Envelopes for censored quantile regression
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DOI：
10.1111/sjos.12602
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发表时间：
2022-05
影响因子：
1
通讯作者：
Yue Zhao;I. Van Keilegom;Shanshan Ding
中科院分区：
文献类型：
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作者：
Yue Zhao;I. Van Keilegom;Shanshan Ding
利用包络模型给出了删失分位数回归模型中系数的一种有效估计。包络模型使用降维技术来识别数据中的物质成分和非物质成分，并仅基于物质成分形成估计器，从而降低了估计的可变性。我们将证明我们所提出的包络估计量相对于传统的删失分位数回归估计量具有有保证的渐近效率增益。我们的分析始于传统上依赖于半参数Z$$Z$$估计的局部加权方法，该估计涉及条件Kaplan-Meier估计。相反，我们将调用独立同分布(I.I.D.)Kaplan-Meier估计量的表示，它消除了这种无限维扰扰，并将Z$$Z$$估计中的目标函数转换为仅由欧几里得参数索引的U$$U$$过程。修正后的Z$$Z$$-估计问题完全是参数问题，因此更易于分析。我们也会重新考虑身份证明。条件Kaplan-Meier估计量的表示。
We propose an efficient estimator for the coefficients in censored quantile regression using the envelope model. The envelope model uses dimension reduction techniques to identify material and immaterial components in the data, and forms the estimator based only on the material component, thus reducing the variability of estimation. We will demonstrate the guaranteed asymptotic efficiency gain of our proposed envelope estimator over the traditional estimator for censored quantile regression. Our analysis begins with the local weighing approach that traditionally relies on semiparametric Z$$ Z $$ ‐estimation involving the conditional Kaplan–Meier estimator. We will instead invoke the independent identically distributed (i.i.d.) representation of the Kaplan–Meier estimator, which eliminates this infinite‐dimensional nuisance and transforms our objective function in Z$$ Z $$ ‐estimation into a U$$ U $$ ‐process indexed by only an Euclidean parameter. The modified Z$$ Z $$ ‐estimation problem becomes entirely parametric and hence more amenable to analysis. We will also reconsider the i.i.d. representation of the conditional Kaplan–Meier estimator.