Arbeitspapier
Sparse quantile regression
We consider both l0-penalized and l0-constrained quantile regression estimators. For the l0-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation errors. We also derive analogous results for the l0-constrained estimator. The resulting rates of convergence are minimax-optimal and the same as those for l1-penalized estimators. Further, we characterize expected Hamming loss for the l0-penalized estimator. We implement the proposed procedure via mixed integer linear programming and also a more scalable first-order approximation algorithm. We illustrate the finite-sample performance of our approach in Monte Carlo experiments and its usefulness in a real data application concerning conformal prediction of infant birth weights (with n ≈ 10 to the 3rd and up to p > 10 to the 3rd ). In sum, our l0-based method produces a much sparser estimator than the l1-penalized approach without compromising precision.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP30/20
- Klassifikation
-
Wirtschaft
- Thema
-
quantile regression
sparse estimation
mixed integer optimization
finitesample property
conformal prediction
Hamming distance
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chen, Le-Yu
Lee, Sokbae
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2020
- DOI
-
doi:10.1920/wp.cem.2020.3020
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chen, Le-Yu
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2020
Ähnliche Objekte (12)
Endogeneity quantile regression models: A control function approach
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Nonparametric estimation of an additive quantile regression model
Nonparametric instrumental variables estimation of a quantile regression model
Exact computation of GMM estimators for instrumental variable quantile regression models
Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative
The quantile identifying effects of multivalued treatments
Characterization of the asymptotic distribution of semiparametric M-estimators
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited
Endogeneity quantile regression models: A control function approach
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Nonparametric estimation of an additive quantile regression model
Nonparametric instrumental variables estimation of a quantile regression model
Exact computation of GMM estimators for instrumental variable quantile regression models
Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative
The quantile identifying effects of multivalued treatments
Characterization of the asymptotic distribution of semiparametric M-estimators
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited
Endogeneity quantile regression models: A control function approach
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Nonparametric estimation of an additive quantile regression model
Nonparametric instrumental variables estimation of a quantile regression model
Exact computation of GMM estimators for instrumental variable quantile regression models
Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative
The quantile identifying effects of multivalued treatments
Characterization of the asymptotic distribution of semiparametric M-estimators