When modeling the kink regression model, it is possible to have an excessive number of explanatory variables and their corresponding coefficients, thereby leading to the over-parameterization and multicollinearity problems. Motivated by these problems, five sparse estimation methods, namely LASSO, sparse Ridge, SCAD, MCP, and Bridge, are considered to perform simultaneous variable selection and parameter estimation, as alternatives to the Ordinary Least Squares (OLS), in the kink regression model. To compare the performance of these sparse estimators, both simulation and real data applications are proposed. According to the simulation results, we demonstrate the superior performance of sparse estimations in terms of selection accuracy and prediction by comparing them to the non-sparse estimations. However, it is not apparent which sparse estimations are more appropriate for estimating the kink regression. However, in an application study, the comparison result indicates that the SCAD penalty would be a preferable penalty function for the application of kink regression to the life expectancy data as the lowest EBIC and the highest \({\text{Adj - }}R^{2}\) are obtained.

在对扭结回归模型进行建模时，可能会有过多的解释变量及其相应的系数，从而导致过度参数化和多重共线性问题。受这些问题的影响，在扭结回归模型中，五种稀疏估计方法（即LASSO，稀疏Ridge，SCAD，MCP和Bridge）被认为可以同时进行变量选择和参数估计，以替代普通最小二乘（OLS）。 。为了比较这些稀疏估计器的性能，提出了仿真和实际数据应用程序。根据仿真结果，我们通过与非稀疏估计进行比较，证明了稀疏估计在选择准确性和预测方面的优越性能。然而，尚不清楚哪种稀疏估计更适合估计纽结回归。但是，在一项应用研究中，比较结果表明，对于将kink回归应用于预期寿命数据（最低EBIC和最高EBIC），SCAD惩罚将是更可取的惩罚函数。\（{\ text {Adj-}} R ^ {2} \）已获得。