scholarly journals Finite sample change point inference and identification for high‐dimensional mean vectors

2020 ◽  
Author(s):  
Mengjia Yu ◽  
Xiaohui Chen
Keyword(s):  
Change Point ◽  
High Dimensional ◽  
Finite Sample ◽  
Mean Vectors

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals High dimensional efficiency with applications to change point tests

2018 ◽  
Vol 12 (1) ◽  
pp. 1901-1947 ◽  
Author(s):  
John A.D. Aston ◽  
Claudia Kirch
Keyword(s):  
Change Point ◽  
High Dimensional

scholarly journals WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

2021 ◽  
Author(s):  
Kamil Faber ◽  
Roberto Corizzo ◽  
Bartlomiej Sniezynski ◽  
Michael Baron ◽  
Nathalie Japkowicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
High Dimensional ◽  
Point Detection

On high-dimensional change point problem

2016 ◽  
Vol 59 (12) ◽  
pp. 2355-2378
Author(s):  
BaiSuo Jin ◽  
GuangMing Pan ◽  
Qing Yang ◽  
Wang Zhou
Keyword(s):  
Change Point ◽  
Dimensional Change ◽  
High Dimensional ◽  
Point Problem ◽  
Change Point Problem

Unsupervised online change point detection in high-dimensional time series

2019 ◽  
Vol 62 (2) ◽  
pp. 719-750 ◽  
Author(s):  
Masoomeh Zameni ◽  
Amin Sadri ◽  
Zahra Ghafoori ◽  
Masud Moshtaghi ◽  
Flora D. Salim ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
High Dimensional ◽  
Point Detection

An adaptive spatial-sign-based test for mean vectors of elliptically distributed high-dimensional data

2019 ◽  
Vol 12 (1) ◽  
pp. 93-106
Author(s):  
Bu Zhou ◽  
Jia Guo ◽  
Jianwei Chen ◽  
Jin-Ting Zhang
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Mean Vectors

scholarly journals High-dimensional regression adjustments in randomized experiments

2016 ◽  
Vol 113 (45) ◽  
pp. 12673-12678 ◽  
Author(s):  
Stefan Wager ◽  
Wenfei Du ◽  
Jonathan Taylor ◽  
Robert J. Tibshirani
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
High Dimensional ◽  
Randomized Experiments ◽  
Finite Sample ◽  
Simple Method ◽  
Average Treatment ◽  
Population Average ◽  
High Dimensional Regression ◽  
Effect Estimation

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample–unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.

Sign in / Sign up

Export Citation Format

Share Document