seminars:stat:141211
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| + | We considered parametric estimation and error estimation in | ||
| + | two classical regression models. First, a heteroscedastic linear | ||
| + | regression model is considered where responses are allowed to be | ||
| + | missing at random and with conditional variance modeled as a function | ||
| + | of the mean response. Maximum empirical likelihood estimation is | ||
| + | studied for an empirical likelihood with an increasing number of | ||
| + | estimated constraints. The resulting estimator is shown to be | ||
| + | asymptotically normal and can perform outperform the ordinary least | ||
| + | squares estimator. Second, we proved a stochastic expansion for a | ||
| + | residual-based estimator of the error distribution in semi-parametric | ||
| + | model. It implies a functional central limit theorem. | ||
| + | </ | ||
