Semiparametric analysis in double-sampling designs via empirical likelihood
Double-sampling designs are commonly used in real applications when it is infeasible to collect exact measurements on all variables of interest. Two samples, a primary sample on proxy measures and a validation subsample on exact measures, are available in these designs. We assume that the validation sample is drawn from the primary sample by the Bernoulli sampling with equal selection probability. An empirical likelihood based approach is proposed to estimate the parameters of interest. By allowing the number of constraints to grow as the sample size goes to infinity, the resulting maximum empirical likelihood estimator is asymptotically normal and its limiting variance-covariance matrix reaches the semiparametric efficiency bound. Moreover, the Wilks-type result of convergence to chi-squared distribution for the empirical likelihood ratio based test is established. Some simulation studies are carried out to assess the finite sample performances of the new approach.
Year of publication: |
2011
|
---|---|
Authors: | Yu, Wen |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 102.2011, 9, p. 1302-1314
|
Publisher: |
Elsevier |
Keywords: | Doubling sampling Empirical likelihood Growing constraints Missing completely at random Semiparametric efficiency Wilks theorem |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Position/force control of robot manipulators using reinforcement learning
PerrusquÃa, Adolfo, (2019)
-
Information Content of Offer Date Revelations: A Fresh Look at Seasoned Equity Offerings
Chan, Konan, (2018)
-
Stock splits, trading continuity, and the cost of equity capital
Lin, Ji-chai, (2009)
- More ...