Programming multiple regression

Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression

In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

Shuffled Frog-Leaping Programming for Solving Regression Problems

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffl...

Parametric Regression Through Genetic Programming

Parametric regression in genetic programming can substantially speed up the search for solutions. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior under a wide ...

Isotonic Regression by Dynamic Programming

Genetic Programming for Symbolic Regression

Genetic programming (GP) is a supervised learning method motivated by an analogy to biological evolution. GP creates successor hypotheses by repeatedly mutating and crossovering parts of the current best hypotheses, with expectation to find a good solution in the evolution process. In this report, the task to be performed was a symbolic regression problem, which is to find the symbolic function...