NON-PARAMETRIC REGRESSION FOR ESTIMATING TOTALS IN FINITE POPULATIONS

By Alan Dorfman. 2002

46 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/db3a41afea7f4c496ca8dc41d7319997fce5c8ea

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1. F. J. Breidt
2. Gerda Claeskens
3. Jean D. Opsomer
4. Xinyu Zhang
5. Nils Lid Hjort
6. Hua Liang
7. J. R. Magnus
8. Mark F. J. Steel

Citations in the corpus

• Model-assisted estimation for complex surveys using penalised splines (2005, methodology): “134), model-assisted local linear (Breidt & Opsomer, 2000), model-based penalised spline (Dorfman, 1992; Zheng & Little, 2003), and model-assisted penalised spline as defined in equation (9).”
• Local polynomial regresssion estimators in survey sampling (2000, background): “…of several estimators: LPR0 local polynomial with q = 0 equation (7) LPR1 local polynomial with q = 1 equation (7) KERN model-based nonparametric Dorfman (1992) CDW bias-calibrated nonparametric Chambers, Dorfman, and Wehrly (1993) The rst four estimators are parametric estimators…”
• Inference for the Population Total from Probability-Proportional-to-Size Samples Based on Predictions from a Penalized Spline Nonparametric Model (2003, unknown): “In particular, Dorfman (1992) and Chambers, Dorfman, and Wehrly (1993) estimate a finite population total by a nonparametric model relating Y to an auxiliary variable, using kernel smoothing.”