Model-assisted estimation for complex surveys using penalised splines

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Model-Assisted Survey Regression Estimation with the Lasso (2017, methodology): “…of the working model lead to model-assisted estimators based on local polynomial regression (Breidt and Opsomer 2000), penalized splines (Breidt, Claeskens, and Opsomer 2005; McConville and Breidt 2013), neural networks (Montanari and Ranalli 2005b), regression splines (Goga 2005), and…”
Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model (2003, unknown): “In particular, Breidt, Claeskens, and Opsomer (2004) propose the GR estimator with the linear model replaced by a penalized spline on covariates.”
Endogenous post-stratification in surveys: Classifying with a sample-fitted model (2008, methodology): “variables) and as (−3,6) for the “high noise” case (σ =0.50 for the other variables). These levels will be denoted as the “steep” and the “ﬂat” model. Two, four and six equal-size strata partitioning [0,1] are considered for the PSE and EPSE. All estimators remain as in Section 4.1. Table 2 displays”