| 1. | The maximum likelihood estimator in logistic regression is a GP.
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| 2. | Is there a way to use maximum likelihood estimators between two variables?
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| 3. | M-estimators are a generalization of maximum likelihood estimators ( MLEs ).
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| 4. | A maximum likelihood estimator coincides with the parameters.
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| 5. | The Ma-Sandri-Sarkar maximum likelihood estimator is currently the best known estimator.
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| 6. | Maximum likelihood estimators ( MLE ) are thus a special case of M-estimators.
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| 7. | NLOGIT is a full information maximum likelihood estimator for a variety of multinomial choice models.
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| 8. | Another estimator which is asymptotically normal and efficient is the maximum likelihood estimator ( MLE ).
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| 9. | This can be solved by iteration, and the maximum likelihood estimator for \ pi is given by
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| 10. | Also, while the maximum likelihood estimator is asymptotically efficient, it is relatively inefficient for small samples.
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