- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- BIRS Workshop Lecture Videos /
- Adaptive confidence sets in shape restricted regression
Open Collections
BIRS Workshop Lecture Videos
BIRS Workshop Lecture Videos
Adaptive confidence sets in shape restricted regression Bellec, Pierre C
Description
We construct adaptive confidence sets in isotonic and convex regression. In univariate isotonic regression, if the true parameter is piecewise constant with $k$ pieces, then the Least-Squares estimator achieves a parametric rate of order $k/n$ up to logarithmic factors. We construct honest confidence sets that adapt to the unknown number of pieces of the true parameter. The proposed confidence set enjoys uniform coverage over all non-decreasing functions. Furthermore, the squared diameter of the confidence set is of order $k/n$ up to logarithmic factors, which is optimal in a minimax sense. In univariate convex regression, we construct a confidence set that enjoys uniform coverage and such that its diameter is of order $q/n$ up to logarithmic factors, where $q-1$ is the number of changes of slope of the true regression function. We will also discuss application of the presented techniques to sparse linear regression.
Item Metadata
Title |
Adaptive confidence sets in shape restricted regression
|
Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
|
Date Issued |
2018-01-31T09:49
|
Description |
We construct adaptive confidence sets in isotonic and convex regression. In univariate isotonic regression, if the true parameter is piecewise constant with $k$ pieces, then the Least-Squares estimator achieves a parametric rate of order $k/n$ up to logarithmic factors. We construct honest confidence sets that adapt to the unknown number of pieces of the true parameter. The proposed confidence set enjoys uniform coverage over all non-decreasing functions. Furthermore, the squared diameter of the confidence set is of order $k/n$ up to logarithmic factors, which is optimal in a minimax sense. In univariate convex regression, we construct a confidence set that enjoys uniform coverage and such that its diameter is of order $q/n$ up to logarithmic factors, where $q-1$ is the number of changes of slope of the true regression function. We will also discuss application of the presented techniques to sparse linear regression.
|
Extent |
50 minutes
|
Subject | |
Type | |
File Format |
video/mp4
|
Language |
eng
|
Notes |
Author affiliation: Rutgers
|
Series | |
Date Available |
2018-07-31
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
DOI |
10.14288/1.0369258
|
URI | |
Affiliation | |
Peer Review Status |
Unreviewed
|
Scholarly Level |
Faculty
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International