Accelerating the pool-adjacent-violators algorithm for isotonic distributional regression

06/09/2020
by   Alexander Henzi, et al.
0

In this note we describe in detail how to apply the pool-adjacent-violators algorithm (PAVA) efficiently in the context of estimating stochastically ordered distribution functions. The main idea is that the solution of a weighted monotone least squares problem changes only little if one component of the target vector to be approximated is changed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/26/2014

An Ordered Lasso and Sparse Time-Lagged Regression

We consider regression scenarios where it is natural to impose an order ...
research
04/09/2019

Optimal solutions to the isotonic regression problem

In general, the solution to a regression problem is the minimizer of a g...
research
08/30/2021

Generalized nearly isotonic regression

The problem of estimating a piecewise monotone sequence of normal means ...
research
09/09/2019

Isotonic Distributional Regression

Isotonic distributional regression (IDR) is a powerful nonparametric tec...
research
06/01/2021

L_0 Isotonic Regression With Secondary Objectives

We provide algorithms for isotonic regression minimizing L_0 error (Hamm...
research
04/22/2010

Oil Price Trackers Inspired by Immune Memory

We outline initial concepts for an immune inspired algorithm to evaluate...

Please sign up or login with your details

Forgot password? Click here to reset