Regression Analysis with Incomplete Covariate
| Autor: | Chen, Yi-Hau770, 程毅豪 |
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| Rok vydání: | 1997 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 85 In the applied regression analysis, data on some components of the regressors may be incomplete for parts of study subjects due to missingness or measurement error. Discarding the incomplete observations may lead to severe bias and efficiency loss for the estimation of the parameters. Two new classes of estimating functions which incorporate both the complete and incomplete data to estimate the regression coefficients are proposed in this dissertation: one is a weighted estimating function which combines complete and incomplete data with a weight accounting for heteroscedastic variations from the two sources of data, and the other is a simultaneous estimating function which estimates all the relevant parameters simultaneously. Both the two proposed estimating functions yield consistent estimates for regression coefficients without the need to specify a correct model for the incomplete covariates, and do not suffer the ''curse of dimensionality'' encountered in the existing nonparametric methods. Simulation study reveal that the efficiency property of the proposed estimators is satisfactory. In particular, the proposed methodologies are computationally convenient and may be widely practicable in daily data analysis. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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