A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
- Title
- A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
- Creator
- Kalaivani, S.
- Description
- Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software. 2025, Gnedenko Forum. All rights reserved.
- Source
- Reliability: Theory and Applications;Volume;20;Issue;1;pp.573-579
- Date
- 01-01-2025
- Publisher
- Gnedenko Forum
- Subject
- data depth; inference; outliers; robust procedures
- Coverage
- Kalaivani S., Department of Statistics and Data Science, Christ University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 19322321;
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Kalaivani, S., “A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/23414.