Classification of intermittent dependent observations
By: P.A. Jacobs, and D.P. Gaver
Contributors: Gaver, Donald Paul; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1990
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 44
Subjects: Cauchy problem; Signatures; Classification
Other titles: NPS-OR-91-02.
Description: Each of J items has a characteristic Signature which varies in time. At time 0, the value of a      Signature and the identity of the corresponding item are known. No further values of      Signatures are observed until a later time T greater than 0. At time t a Signature associated      with an unknown item is observed. The problem is to estimate the identity of the item whose      Signature is observed at time t. The estimation procedure studied is to estimate the identity of      the item that is associated with the Signature at time t to be that one which maximizes the      posterior probability of being associated with the observed Signature. Univariate and      multivariate Gaussian and univariate Cauchy autoregressive processes are considered as      models for the Signatures. The robustness of the univariate Gaussian, (respectively Cauchy),      procedure when applied to Cauchy, (respectively Gaussian) data is studied. The results      suggest that the Gaussian classification procedure is biased towards classifying the Signature      observed at time t as being associated with the same item that is associated with the Signature      at time 0. The Cauchy procedure is biased towards classifying a Signature observed at time t      as being associated with a different item than the one associated with the Signature at time 0.
Notes: Title from cover.

"NPS-OR-91-02."

"October 1990."

Includes bibliographical references (p. 33)

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