In this work we compare nonparametric estimators for finite population distribution functions based on two types of fitted values: the fitted values from the well-known Kuo estimator and a modified version of them, which incorporates a nonparametric estimate for the mean regression function. For each type of fitted values we consider the corresponding model-based estimator and, after incorporating design weights, the corresponding generalized difference estimator. We show under fairly general conditions that the leading term in the model mean square error is not affected by the modification of the fitted values, even though it slows down the convergence rate for the model bias. Second order terms of the model mean square errors are difficult to obtain and will not be derived in the present paper. It remains thus an open question whether the modified fitted values bring about some benefit from the model-based perspective. We discuss also design-based properties of the estimators and propose a variance estimator for the generalized difference estimator based on the modified fitted values. Finally, we perform a simulation study. The simulation results suggest that the modified fitted values lead to a considerable reduction of the design mean square error if the sample size is small.

Pasquazzi, L., DE CAPITANI, L. (2016). A comparison between nonparametric estimators for finite population distribution functions. SURVEY METHODOLOGY, 42(1), 87-120.

A comparison between nonparametric estimators for finite population distribution functions

PASQUAZZI, LEO
;
DE CAPITANI, LUCIO
Secondo
2016

Abstract

In this work we compare nonparametric estimators for finite population distribution functions based on two types of fitted values: the fitted values from the well-known Kuo estimator and a modified version of them, which incorporates a nonparametric estimate for the mean regression function. For each type of fitted values we consider the corresponding model-based estimator and, after incorporating design weights, the corresponding generalized difference estimator. We show under fairly general conditions that the leading term in the model mean square error is not affected by the modification of the fitted values, even though it slows down the convergence rate for the model bias. Second order terms of the model mean square errors are difficult to obtain and will not be derived in the present paper. It remains thus an open question whether the modified fitted values bring about some benefit from the model-based perspective. We discuss also design-based properties of the estimators and propose a variance estimator for the generalized difference estimator based on the modified fitted values. Finally, we perform a simulation study. The simulation results suggest that the modified fitted values lead to a considerable reduction of the design mean square error if the sample size is small.
Articolo in rivista - Articolo scientifico
Finite population sampling; Distribution function estimator; Fitted values; Kuo estimator
English
2016
42
1
87
120
open
Pasquazzi, L., DE CAPITANI, L. (2016). A comparison between nonparametric estimators for finite population distribution functions. SURVEY METHODOLOGY, 42(1), 87-120.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/127434
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