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Abstract Details
Activity Number:
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625
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #305429 |
Title:
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Model-Assisted Lasso Regression Estimator
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Author(s):
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Kelly McConville*+ and Jay Breidt and Thomas Lee
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Companies:
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Whitman College and Colorado State University and University of California at Davis
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Address:
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122 Otis Street, Walla Walla, WA, 99362-2076, United States
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Keywords:
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complex surveys ;
model selection ;
lasso
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Abstract:
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In natural resource surveys, a substantial amount of auxiliary information, typically derived from remotely-sensed imagery and organized in the form of spatial layers in a geographic information system (GIS), is available. Some of this ancillary data may be extraneous and a sparse model would be appropriate. Model selection methods are therefore warranted. The `least absolute shrinkage and selection operator' (lasso) conducts model selection and parameter estimation simultaneously by penalizing the sum of the absolute values of the model coefficients. A survey-weighted lasso criterion, which accounts for the sampling design, is derived and a survey-weighted lasso estimator is presented. The root-n design consistency of the estimator and a central limit theorem result are proved. Several variants of the survey-weighted lasso estimator are constructed. In particular, a calibration estimator and a ridge regression approximation estimator are constructed to produce weights that can be applied to several study variables. Simulation studies show the lasso estimators are more efficient than the regression estimator when the true model is sparse.
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