Asset Details
Do you wish to reserve the book?
Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?
Do you wish to request the book?
Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?
2006
Overview
We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined procedure resulting from first selecting a model (e.g., by a model selection criterion such as AIC or by a hypothesis testing procedure) and then estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data set. We show that it is impossible to estimate this distribution with reasonable accuracy even asymptotically. In particular, we show that no estimator for this distribution can be uniformly consistent (not even locally). This follows as a corollary to (local) minimax lower bounds on the performance of estimators for this distribution. Similar impossibility results are also obtained for the conditional distribution of linear functions (e.g., predictors) of the post-model-selection estimator.
Publisher
Institute of Mathematical Statistics,The Institute of Mathematical Statistics
Subject
/ 62F10
/ 62F12
/ 62J05
/ 62J07
/ Akaike’s information criterion AIC
/ Exact sciences and technology
/ Inference after model selection
/ Inference from stochastic processes; time series analysis
/ Insurance, economics, finance
/ Matrices
/ Modeling
/ Post-Model-Selection Estimation
/ post-model-selection estimator
/ Sciences and techniques of general use
/ Studies
We currently cannot retrieve any items related to this title. Kindly check back at a later time.