Estimation and inference in generalised linear models with constrained iteratively-reweighted least squares (2509.18406v1)

Abstract: We propose a simple and flexible framework for generalised linear models (GLM) with linear constraints on the coefficients. Linear constraints are useful in a wide range of applications, allowing the fitting of model with high-dimensional or highly collinear predictors, as well as encoding assumptions on the association between some or all predictors and the response. We propose the constrained iteratively-reweighted least squares (CIRLS) to fit the model, iterating quadratic programs to ensure the coefficient vector remains feasible according to the constraints. Inference for constrained coefficients can be obtained by simulating from a truncated multivariate normal distribution and computing empirical confidence intervals or variance-covariance matrix from the simulated coefficient vectors. We additionally discuss the complexity of a constrained GLM, proposing a measure of expected degrees of freedom which accounts for the stringency of constraints in the reduction of the model degrees of freedom. An extensive simulations study shows that constraining the coefficients introduces some bias to the estimation, but also decreases the estimator variance. This trade-off results in an improved estimator when constraints are chosen appropriately. The simulations also show that our proposed inference results in error in variance estimation and coverage. The proposed framework is illustrated on two case studies, showing its usefulness as well as some of its weaknesses.