Accelerating the Pool-Adjacent-Violators Algorithm for Isotonic Distributional Regression

被引：7

作者：

Henzi, Alexander ^{[1
]}

Moesching, Alexandre ^{[2
]}

Duembgen, Lutz ^{[1
]}

机构：

[1] Univ Bern, Bern, Switzerland

[2] Georg August Univ Gottingen, Gottingen, Germany

来源：

METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY | 2022年 / 24卷 / 04期

基金：

瑞士国家科学基金会;

关键词：

Monotone regression; Sequential computation; Weighted least squares;

D O I：

10.1007/s11009-022-09937-2

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In the context of estimating stochastically ordered distribution functions, the pool-adjacent-violators algorithm (PAVA) can be modified such that the computation times are reduced substantially. This is achieved by studying the dependence of antitonic weighted least squares fits on the response vector to be approximated.

引用

页码：2633 / 2645

页数：13

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