Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00440-004-0384-5
Title: The broken sample problem
Authors: Bai, Z. 
Hsing, T.
Keywords: Consistent estimation
Empirical process
Gaussian process
Kulback-Leibler information
Issue Date: Apr-2005
Citation: Bai, Z., Hsing, T. (2005-04). The broken sample problem. Probability Theory and Related Fields 131 (4) : 528-552. ScholarBank@NUS Repository. https://doi.org/10.1007/s00440-004-0384-5
Abstract: Suppose that (X i Y i )i=12 ... n are iid. random vectors with uniform marginals and a certain joint distribution F ρ where ρ is a parameter with ρ=ρ o corresponds to the independence case. However the X's and Y's are observed separately so that the pairing information is missing. Can ρ be consistently estimated? This is an extension of a problem considered in (1980) which focused on the bivariate normal distribution with ρ being the correlation. In this paper we show that consistent discrimination between two distinct parameter values ρ 1 and ρ 2 is impossible if the density f ρ of F ρ is square integrable and the second largest singular value of the linear operator [InlineMediaObject not available: see fulltext.] is strictly less than 1 for ρ=ρ 1 and ρ 2. We also consider this result from the perspective of a bivariate empirical process which contains information equivalent to that of the broken sample. © Springer-Verlag 2004.
Source Title: Probability Theory and Related Fields
URI: http://scholarbank.nus.edu.sg/handle/10635/105410
ISSN: 01788051
DOI: 10.1007/s00440-004-0384-5
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