Mathematical Sciences: Partial Ancillarity, Partial         Sufficiency and Generalized Principles for Inference
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Alternative definitions of partial ancillarity, in the                     presence of nuisance parameters, will be examined with regard to                loss of information involved when inference is based on the                     conditional distribution given the values of such partial                       ancillary statistics.  Similarly, definitions of partial                        sufficiency will be examined with respect to loss of information                when inference is based on the marginal distribution of such                    partial sufficiency statistics.  Finally, a generalized                         likelihood principle will be formulated for inference concerning                parameters of interest in the presence of the nuisance                          parameters.                                                                          This is foundational research in statistics for data                       structures confounded with variables other than those of research               interest.  Statistical methods based on partial information have                become quite popular in practice and this foundational work is                  needed and timely.
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{'Code': '1269', 'Text': 'STATISTICS'}
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