seminars:stat:2220414
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| + | In research and statistics projects, if we know the form of the | ||
| + | regression, parametric inferences often perform better than semiparametric | ||
| + | inferences and nonparametric inferences. This talk focus on the maximum | ||
| + | likelihood estimator (MLE) under the uniform distribution with linear | ||
| + | regression data. We first introduce and discuss the assumptions and the | ||
| + | conclusions in Robbins, H. and Zhang, C.-H.' | ||
| + | the closed form algorithm to find the MLE under the multiple linear | ||
| + | regression model. Some results on this subject with the simple linear | ||
| + | regression and non-random covariate are introduced and compared. | ||
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