CONSTRAINED PARTIAL LINEAR REGRESSION SPLINES
CONSTRAINED PARTIAL LINEAR REGRESSION SPLINES
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DOI：
10.5705/ss.202016.0342
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发表时间：
2018-01-01
影响因子：
1.4
通讯作者：
Meyer, Mary C.
中科院分区：
文献类型：
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作者：
Meyer, Mary C.
约束的局部线性模型使用单锥投影进行拟合，不需要反拟合。圆锥公式不仅提供了有效的计算，而且还允许推导收敛速度和推理方法。给出了参数同时根-n收敛和回归函数最优收敛的条件。涉及非线性回归函数的假设检验，在控制线性项的影响的同时，在正态误差假设下使用检验统计量，其零分布是贝塔随机变量的混合分布。涉及线性项的推理使用近似的t和F分布；模拟表明，与竞争对手相比，这些分布表现得很好。
The constrained partial linear model is fit using a single cone projection, without back-fitting. The cone formulation not only provides efficient computation, but also allows for derivation of convergence rates and inference methods. Conditions for simultaneous root-n convergence of the parameters and optimal convergence for the regression function are given. Hypothesis tests involving the nonlinear regression function, while controlling for the effects of the linear term, use a test statistic whose null distribution is that of a mixture-of-betas random variables, under the normal errors assumption. Inference involving the linear term uses approximate t and F distributions; simulations show these perform well compared to competitors.