Online Program Home
  My Program

Abstract Details

Activity Number: 668 - Estimation with Statistical Models
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #322489 View Presentation
Title: Outlier Robust Estimation of the Total Private Cost of Payment Methods for Large Businesses
Author(s): Valery Dongmo Jiongo*
Companies: Bank of Canada
Keywords: Conditional bias ; Parametric bootstrap ; Mean square errors ; Combining samples ; Total private costs ; Outliers
Abstract:

The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods (RCPM) faced low response rates and outliers in sample data for two of its strata: chains and large independent businesses. In the presence of outliers, Chambers (1986) and Beaumont, Haziza and Ruiz-Gazen (2013) suggested the use of a bias-corrected estimator based on an appropriate choice of the tuning constant. In this paper, we develop a parametric bootstrap procedure to determine the tuning constant which minimise an estimator of the mean square errors (MSE). Monte Carlo simulations are performed to compare the performance of the robust-estimators for several choice of the tuning constant, including the adaptive method proposed by Beaumont, Haziza and Ruiz-Gazen (2013). The results show that for all the simulation set-up considered in this paper, the estimator based on the conditional bias with the adaptive choice of the tuning constant proposed by Beaumont, Haziza and Ruiz-Gazen (2013) performs better in terms of both bias and efficiency. The methodology is applied to the RCPM survey.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association