An Inverse Norm Sign Test of Location Parameter for High-Dimensional Data
Bibliographic Data
| ID | 19418009 |
|---|---|
| Authors | Long Feng (0000-0002-9623-2345, Northeast Normal University), Binghui Liu (0000-0002-9331-8389, Northeast Normal University, corresponding author), Yanyuan Ma (0000-0001-6985-0351, Department of Statistics, Pennsylvania State University, University Park, PA) |
| Year | 2021 |
| Volume | 39 |
| Issue | 3 |
| Pages | 807-815 |
| Publication date | 2021-07-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2020.1736084 |
| OpenAlex | W3007511514 |
| Language | EN |
| Citations received | 1 |
| References cited | 27 |
We consider the one sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample size. We devise a novel inverse norm sign test (INST) that is consistent and has much improved power than many existing popular tests. We further construct a general class of weighted spatial sign tests which includes these existing tests, and show that INST is the optimal member within this class, in that it is consistent and is uniformly more powerful than all other members. We establish the asymptotic null distribution and local power property of the class of tests rigorously. Extensive numerical experiments demonstrate the superiority of INST in terms of both efficiency and robustness
Geometry · Inverse · Mathematical analysis · Mathematical optimization · Null distribution · Null hypothesis · Sign test · Statistical hypothesis testing · Statistics · Test statistic · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Applied Mathematics
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |