Controlling the number of regressors in linear equations when constructing a listwise regression model

DOI: 10.21293/1818-0442-2025-28-3-139-144

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Abstract: For clustering an available sample of statistical data, a listwise regression model is proposed that contains a complete set of input variables in each equation of the list. The problem of estimating unknown parameters of this model using the least absolute deviations method is reduced to a mixed 0-1 integer linear programming problem. To control the number of regressors in the list equations, the optimization problem is extended with additional constraints. Solving this problem makes it possible to obtain the best subset of regressors included in the list equations, the equations’ coefficients, and the switching rule between them. Computational experiments were carried out to confirm the correctness of the developed mathematical approach.

Keywords: regression analysis, listwise regression, clustering, least absolute deviations method, subset selection, mixed 0-1 integer linear programming problem, integer floor function

For citation:
Bazilevskiy M. P. Controlling the number of regressors in linear equations when constructing a listwise regression model. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 3, pp. 139–144. DOI: 10.21293/1818-0442-2025-28-3-139-144

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