Model-assisted estimation for complex surveys using penalised splines Export

@article{citeulike:397167, abstract = {Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalised spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. They allow straightforward extensions to multiple auxiliary variables and to complex designs. Under standard design conditions, the estimators are design consistent and asymptotically normal, and they admit consistent variance estimation using familiar design-based methods. Data-driven penalty selection is considered in the context of unequal probability sampling designs. Simulation experiments show that the estimators are more efficient than parametric regression estimators when the parametric model is incorrectly specified, while being approximately as efficient when the parametric specification is correct. An example using Forest Health Monitoring survey data from the U.S. Forest Service demonstrates the applicability of the methodology in the context of a two-phase survey with multiple auxiliary variables. 10.1093/biomet/92.4.831}, author = {Breidt, F. J. and Claeskens, G. and Opsomer, J. D.}, citeulike-article-id = {397167}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/biomet/92.4.831}, citeulike-linkout-1 = {http://biomet.oxfordjournals.org/content/92/4/831.abstract}, citeulike-linkout-2 = {http://biomet.oxfordjournals.org/content/92/4/831.full.pdf}, citeulike-linkout-3 = {http://www.ingentaconnect.com/content/oup/biomet/2005/00000092/00000004/art00831}, comment = {This paper is just a current example of data-driven methods for model selection---no cross validation....}, day = {1}, doi = {10.1093/biomet/92.4.831}, issn = {0006-3444}, journal = {Biometrika}, keywords = {model, model-assisted, selection, splines, surveys}, month = {December}, number = {4}, pages = {831--846}, posted-at = {2009-11-05 22:52:11}, priority = {2}, publisher = {Oxford University Press}, title = {Model-assisted estimation for complex surveys using penalised splines}, url = {http://dx.doi.org/10.1093/biomet/92.4.831}, volume = {92}, year = {2005} }

TY - JOUR ID - citeulike:397167 L3 - citeulike-article-id:397167 N2 - Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalised spline regression is proposed. These estimators are weighted linear combinations of sample observations, with weights calibrated to known control totals. They allow straightforward extensions to multiple auxiliary variables and to complex designs. Under standard design conditions, the estimators are design consistent and asymptotically normal, and they admit consistent variance estimation using familiar design-based methods. Data-driven penalty selection is considered in the context of unequal probability sampling designs. Simulation experiments show that the estimators are more efficient than parametric regression estimators when the parametric model is incorrectly specified, while being approximately as efficient when the parametric specification is correct. An example using Forest Health Monitoring survey data from the U.S. Forest Service demonstrates the applicability of the methodology in the context of a two-phase survey with multiple auxiliary variables. 10.1093/biomet/92.4.831 IS - 4 JF - Biometrika SN - 0006-3444 EP - 846 TI - Model-assisted estimation for complex surveys using penalised splines PB - Oxford University Press VL - 92 SP - 831 KW - model KW - model-assisted KW - selection KW - splines KW - surveys N1 - This paper is just a current example of data-driven methods for model selection---no cross validation.... AU - Breidt, FJ AU - Claeskens, G AU - Opsomer, JD PY - 2005/12/01/ UR - http://dx.doi.org/10.1093/biomet/92.4.831 ER -