论文标题
稀疏观测曲线的部分最小二乘和测量误差的曲线
Partial least squares for sparsely observed curves with measurement errors
论文作者
论文摘要
功能部分最小二乘（FPLS）通常用于拟合标量在功能回归模型。为了精确，FPLS要求在整个时间范围内尽可能地记录功能预测变量的每个实现。但是，有时在纵向研究和缺失数据研究中会违反这种情况。针对这一点，我们将FPLS调整为场景，在这种情况下，每个受试者的测量数量很少，并且从上方界定。由此产生的建议被缩写为请求。在某些规律性条件下，我们建立了估计器的一致性，并给出标量响应的置信区间。模拟研究和真实数据应用说明了请求的竞争精度
Functional partial least squares (FPLS) is commonly used for fitting scalar-on-function regression models. For the sake of accuracy, FPLS demands that each realization of the functional predictor is recorded as densely as possible over the entire time span; however, this condition is sometimes violated in, e.g., longitudinal studies and missing data research. Targeting this point, we adapt FPLS to scenarios in which the number of measurements per subject is small and bounded from above. The resulting proposal is abbreviated as PLEASS. Under certain regularity conditions, we establish the consistency of estimators and give confidence intervals for scalar responses. Simulation studies and real-data applications illustrate the competitive accuracy of PLEASS
