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Portnoy, Stephen L. Zhao, Quanshui 2011-05-07T13:01:13Z 2011-05-07T13:01:13Z 10000-01-01 1995 The ordinary least squares (OLS) method is known to be efficient for linear models when the errors are homogeneous with Gaussian distributions, but troublesome with heteroscedastic or non-Gaussian errors. For the latter nonstandard case, we use the weighted quantile regression (l$\sb1$) method, gaining both robustness and efficiency, with successful applications to interval forecasting of ARCH type time series models. Dynamically changing regression parameters are another discrepancy to the ordinary linear models. By using the recursive method, the dynamically evolving parameters can be estimated. Asymptotic properties are studied for paired comparison models (a chess rating system) and dynamic ARCH models. Made available in DSpace on 2011-05-07T13:01:13Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9624549.pdf: 3478188 bytes, checksum: 5ba57b2bc968f0188f021610ad42142f (MD5) Previous issue date: 1995 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:49:07Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:22:18-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9624549 (UMI)AAI9624549 http://hdl.handle.net/2142/21193 eng Copyright 1995 Zhao, Quanshui Statistics Estimation and inference for conditionally heteroscedastic models text Statistics Statistics University of Illinois at Urbana-Champaign Dissertation Ph.D.