A Comparison of Robust and Nonparametric Estimators under the Simple Linear Regression Model [electronic resource] / Jonathan Nevitt and Hak P. Tam.
This study investigates parameter estimation under the simple linear regression model for situations in which the underlying assumptions of ordinary least squares estimation are untenable. Classical nonparametric estimation methods are directly compared against some robust estimation methods for con...
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| Format: | Electronic eBook |
| Language: | English |
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Distributed by ERIC Clearinghouse,
1997.
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| Summary: | This study investigates parameter estimation under the simple linear regression model for situations in which the underlying assumptions of ordinary least squares estimation are untenable. Classical nonparametric estimation methods are directly compared against some robust estimation methods for conditions in which varying degrees of outliers are present in the observed data. In addition, estimator performance is considered under conditions in which the normality assumption regarding error distributions is violated. The study addresses the problem through computer simulation methods. The study design includes 3 sample sizes (n=10, 30, 50) crossed with 5 types of error distributions (unit normal, 10% contaminated normal, 30% contaminated normal, lognormal, t-5df). Variance, bias, mean squared error, and relative mean squared error are used to evaluate estimator performance. Recommendations to applied researchers and direction for further study are considered. (Contains 4 tables, 4 figures, and 20 references.) (Author/SLD) |
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| Item Description: | ERIC Document Number: ED410289. ERIC Note: Paper presented at the Annual Meeting of the American Educational Research Association (Chicago, IL, March 24-28, 1997). |
| Physical Description: | 32 p. |