In this work we suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + ϵ, where m(·) belongs to some parametric class {\({m_\beta}(\cdot):\beta \in \mathbb{K}\)} and the error ϵ is independent of the covariate X. The response Y is subject to random right censoring. Using a nonlinear mode regression, a new estimation procedure for the true unknown parameter vector β₀is proposed that extends the classical least squares procedure for nonlinear regression. We also establish asymptotic properties for the proposed estimator under assumptions of the error density. We investigate the performance through a simulation study.

中文翻译：

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带有删失数据的半参数模式回归

在这项工作中，我们假设随机向量（X，Y）满足回归模型Y = m（X）+ ϵ，其中m（·）属于某些参数类{ \（{m_ \ beta}（\ cdot）： \ beta \ in \ mathbb {K} \） }中，误差ϵ与协变量X无关。响应Y受到随机权限检查。使用非线性回归模式，对于真正的未知参数矢量的新估计过程β ₀提出了扩展经典最小二乘法的非线性回归方法。在误差密度的假设下，我们还为拟议的估计量建立了渐近性质。我们通过模拟研究来研究性能。