Abstract
Pooling data from various sources to improve the parameter estimation is an important problem from the practitioner’s perspective. If the pooling procedure is carried out judiciously, a much more efficient estimation strategy can be achieved for the targeted parameter. However, it is imperative that underlying assumptions for pooling the data are investigated thoroughly before merging the data into a single data set, and suggesting a pooled estimator based on a grand data. In this chapter, we explore various estimation strategies for pooling data from several sources. We suggest some efficient estimation strategies based on pretest and James–Stein principles. We consider simultaneous estimation of several coefficients of variation to demonstrate the power and beauty of pretest and shrinkage estimation in pooling data. We investigate the asymptotic and finite sample properties of these estimators using mean squared error criterion. We showcase that the shrinkage estimators based on the James–Stein rule dominate the benchmark estimator of coefficients of variation.
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Ahmed, S.E. (2014). Pooling Data: Making Sense or Folly. In: Penalty, Shrinkage and Pretest Strategies. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-03149-1_3
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DOI: https://doi.org/10.1007/978-3-319-03149-1_3
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