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Comparing Not Necessarily Nested Models with the Minimum Aic and the Maximum Kullback-Leibler Entropy Criteria : New Properties and Connections

By Findley, David F.

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Book Id: WPLBN0000582968
Format Type: PDF eBook
File Size: 1,845,737 KB.
Reproduction Date: 2005
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Title: Comparing Not Necessarily Nested Models with the Minimum Aic and the Maximum Kullback-Leibler Entropy Criteria : New Properties and Connections  
Author: Findley, David F.
Volume:
Language: English
Subject: Government publications, Census., Census report
Collections: U.S. Census Bureau Collection
Historic
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Publisher: U.S. Census Bureau Department

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Findley, D. F. (n.d.). Comparing Not Necessarily Nested Models with the Minimum Aic and the Maximum Kullback-Leibler Entropy Criteria : New Properties and Connections. Retrieved from http://www.hawaiilibrary.net/


Description
Statistical Reference Document

Excerpt
Excerpt: We assume that a q-dimensional process yt is given which is to be linearly regressed upon one or more other processes (stochastic, nonstochastic ?or mixed). If the r-dimensional process xt is a candidate regressor, then several points of view are possible concerning the ?ideal? regression relation to be estimated, (1.1) Yt = A(x)xt + eiX) > beyond the basic assumntions that the error process et(4 has mean zero and a constant, nonsingular covariance matrix ? ...

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