The beta regression model is a useful framework to model response variables that are rates or proportions, that is to say, response variables which are continuous and restricted to the interval (0,1). As with any other regression model, parameter estimates may be affected by collinearity or even perfect collinearity among the explanatory variables. To handle these situations shrinkage estimators are proposed. In particular we develop ridge regression and LASSO estimators from a penalized likelihood perspective with a logit link function. The properties of the resulting estimators are evaluated through a simulation study and a real data application

翻译：Beta回归模型是建模比率或比例响应变量的有效框架，即连续且限制在区间(0,1)内的响应变量。与任何其他回归模型类似，参数估计可能受到解释变量间共线性甚至完全共线性的影响。为处理这些情况，本文提出了收缩估计方法。特别地，我们从惩罚似然的角度出发，采用logit连接函数推导了岭回归和LASSO估计量。通过模拟研究和实际数据应用，评估了所得估计量的统计特性。