USING REGRESSION ANALYSIS TO REDUCE AGGREGATION BIAS IN LINEAR PROGRAMMING SUPPLY MODELS*

SENSITIVITY ANALYSIS IN LINEAR-PLUS-LINEAR FRACTIONAL PROGRAMMING PROBLEMS

In this paper, we study the classical sensitivity analysis when the right - hand – side vector, and the coefficients of the objective function are allowed to vary. 

Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...

Parameter Estimation and Change Detection in Linear Regression Models Using Mixed Integer Linear Programming

We present a method for change detection and parameter estimation in change in the mean models and AR(X) model. The method is based on the assumption of piecewise constant parameters resulting in a sparse structure of their derivative. To illustrate the algorithm and the performance of it, we apply it to the change in the mean model and compare it with four other change detection algorithms. Tw...

Analysis of aggregation error in supply functions based on farm-programming models

Non-Linear Continuum Regression Using Genetic Programming

In this contribution, genetic programming is combined with continuum regression to produce two novel non-linear continuum regression algorithms. The first is a ‘sequential’ algorithm while the second, adopts a ‘team-based’ strategy. Having discussed continuum regression, the modifications required to extend the algorithm for non-linear modelling are outlined. The results of applying the derived...