Lp norm estimation procedures and an L1 norm algorithm for unconstrained and constrained estimation for linear models
When the distribution of the errors in a linear regression model departs from normality, the method of least squares seems to yield relatively poor estimates of the coefficients. One alternative approach to least squares which has received a great deal of attention of late is minimum L<sub>p&l...
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| Format: | Others |
| Language: | en_US |
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Virginia Polytechnic Institute and State University
2015
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| Online Access: | http://hdl.handle.net/10919/53627 |