Methodology and Computing in Applied Probability

, Volume 14, Issue 3, pp 407–420

Stochastic Comparisons of Symmetric Sampling Designs

Article

DOI: 10.1007/s11009-011-9213-3

Cite this article as:
Goldstein, L., Rinott, Y. & Scarsini, M. Methodol Comput Appl Probab (2012) 14: 407. doi:10.1007/s11009-011-9213-3

Abstract

We compare estimators of the integral of a monotone function f that can be observed only at a sample of points in its domain, possibly with error. Most of the standard literature considers sampling designs ordered by refinements and compares them in terms of mean square error or, as in Goldstein et al. (2011), the stronger convex order. In this paper we compare sampling designs in the convex order without using partition refinements. Instead we order two sampling designs based on partitions of the sample space, where a fixed number of points is allocated at random to each partition element. We show that if the two random vectors whose components correspond to the number allocated to each partition element are ordered by stochastic majorization, then the corresponding estimators are likewise convexly ordered. If the function f is not monotone, then we show that the convex order comparison does not hold in general, but a weaker variance comparison does.

Keywords

Convex orderStochastic majorizationStratified samplingExchangeable partitions of integers

AMS 2000 Subject Classification

Primary 60E15; Secondary 62D0565C05

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Larry Goldstein
    • 1
  • Yosef Rinott
    • 2
    • 3
  • Marco Scarsini
    • 4
    • 5
  1. 1.Department of MathematicsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Statistics and Center for the Study of RationalityHebrew University of JerusalemJerusalemIsrael
  3. 3.LUISSRomaItaly
  4. 4.Dipartimento di Scienze Economiche e AziendaliLUISSRomaItaly
  5. 5.HECParisFrance