Monte Carlo Evidence on Adaptive Maximum Likelihood Estimation of a Regression
Hsieh, David A. ; Manski, Charles F.
Ann. Statist., Tome 15 (1987) no. 1, p. 541-551 / Harvested from Project Euclid
Text on Project Euclid
PDF
Alt PDF
This paper reports Monte Carlo evidence on the fixed sample size properties of adaptive maximum likelihood estimates of a linear regression. The focus is on the problem of selecting the smoothing and trimming parameters used in estimating the score function. We examine the performance of adaptive maximum likelihood estimators when these parameters are preselected or, alternatively, are determined by a data-based bootstrap method.
Publié le : 1987-06-14
Classification:  Adaptation,  efficient estimation,  bootstrap,  62E25,  62F11,  62F35,  62J05 
@article{1176350359,
     author = {Hsieh, David A. and Manski, Charles F.},
     title = {Monte Carlo Evidence on Adaptive Maximum Likelihood Estimation of a Regression},
     journal = {Ann. Statist.},
     volume = {15},
     number = {1},
     year = {1987},
     pages = { 541-551},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176350359}
}

Hsieh, David A.; Manski, Charles F. Monte Carlo Evidence on Adaptive Maximum Likelihood Estimation of a Regression. Ann. Statist., Tome 15 (1987) no. 1, pp.  541-551. http://gdmltest.u-ga.fr/item/1176350359/