Estimation of a semiparametric transformation model
By Oliver B. Linton, Stefan Sperlich, I. Van Keilegom. 2008
102 citations recorded by the source corpus.
This paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or multiplicative separability. We give results for the estimation of the transformation when the rest of the model is estimated non- or semi-parametrically and fulfills some consistency conditions. We propose two methods for the estimation of the transformation parameter maximizing a profile likelihood function or minimizing the mean squared distance from independence. First the problem of identification of such models is discussed. We then state asymptotic results for a general class of nonparametric estimators. Finally, we give some particular examples of nonparametric estimators of transformed separable models. The small sample performance is studied in several simulations.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/dda92bdffe6c70e9afdf04e171bb4634a3a0df1d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Torsten Hothorn
- Peter Buhlmann
- Lisa Möst
- Christoph Rothe
- Jan Peters
- Marloes H. Maathuis
- Nicolai Meinshausen
- Sara A. van de Geer
In Statistics and Probability
- Torsten Hothorn
- Peter Buhlmann
- Lisa Möst
- Christoph Rothe
- Sara A. van de Geer
- Nicolai Meinshausen
- Jan Peters
- Robert Tibshirani
Citations in the corpus
- Semiparametric estimation of binary response models with endogenous regressors (2009, methodology): “2 in Chen, Linton, and Van Keilegom (2003) are fulfilled, which in turn implies the statement of the”
- Most Likely Transformations (2015, methodology): “The simultaneous estimation of FZ in the model P(Y ≤ y | X = x) = FZ(hY (y) − x̃>β) was studied by Horowitz (1996) and later extended by Linton et al. (2008) to non-linear functions hx with parametric baseline transformation hY and kernel estimates for FZ and hx.”
- SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES (2014, unknown): “See also Linton et al. (2008).”