## Statistics Surveys

- Statist. Surv.
- Volume 10 (2016), 53-99.

### Fundamentals of cone regression

#### Abstract

Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.

#### Article information

**Source**

Statist. Surv., Volume 10 (2016), 53-99.

**Dates**

Received: March 2015

First available in Project Euclid: 19 May 2016

**Permanent link to this document**

https://projecteuclid.org/euclid.ssu/1463663054

**Digital Object Identifier**

doi:10.1214/16-SS114

**Mathematical Reviews number (MathSciNet)**

MR3506106

**Zentralblatt MATH identifier**

1384.62134

**Subjects**

Primary: 62

Secondary: 90

**Keywords**

cone regression linear complementarity problems proximal operators

#### Citation

Dimiccoli, Mariella. Fundamentals of cone regression. Statist. Surv. 10 (2016), 53--99. doi:10.1214/16-SS114. https://projecteuclid.org/euclid.ssu/1463663054

#### Supplemental materials

- Implementation in Scilab and Matlab of the algorithms reviewed in “Fundamentals of cone regression”. Digital Object Identifier: doi:10.1214/16-SS114SUPP