A flexible sequential Monte Carlo algorithm for parametric constrained regression
- Resource Type
- Authors
- Berwin A. Turlach; Kenyon Ng; Kevin Murray
- Source
- Computational Statistics & Data Analysis. 138:13-26
- Subject
- FOS: Computer and information sciences
Statistics and Probability
Computer science
Applied Mathematics
05 social sciences
Regression analysis
Monotonic function
Function (mathematics)
Rational function
Statistics - Computation
01 natural sciences
Methodology (stat.ME)
010104 statistics & probability
Computational Mathematics
Computational Theory and Mathematics
Indicator function
0502 economics and business
Simulated annealing
0101 mathematics
Particle filter
Algorithm
Statistics - Methodology
Computation (stat.CO)
050205 econometrics
Parametric statistics
- Language
- ISSN
- 0167-9473
An algorithm is proposed that enables the imposition of shape constraints on regression curves, without requiring the constraints to be written as closed-form expressions, nor assuming the functional form of the loss function. This algorithm is based on Sequential Monte Carlo-Simulated Annealing and only relies on an indicator function that assesses whether or not the constraints are fulfilled, thus allowing the enforcement of various complex constraints by specifying an appropriate indicator function without altering other parts of the algorithm. The algorithm is illustrated by fitting rational function and B-spline regression models subject to a monotonicity constraint. An implementation of the algorithm using R is freely available on GitHub.
Comment: Typo corrections. Code available on https://github.com/weiyaw/blackbox