Please use this identifier to cite or link to this item:
https://rfos.fon.bg.ac.rs/handle/123456789/2993Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Aleksić, Danijel | en_US |
| dc.date.accessioned | 2025-12-09T08:02:34Z | - |
| dc.date.available | 2025-12-09T08:02:34Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://rfos.fon.bg.ac.rs/handle/123456789/2993 | - |
| dc.description.abstract | Little’s MCAR test is a very popular method for determining if data is missing completely at random (MCAR). A test for MCAR is constructed in the special case of monotone missing data. The test is based on the estimator of the covariation of the data itself and the response indicator of complete data columns. A connection between Little’s and novel statistics is established and those are compared from various points of view. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | ECOSTA ECONOMETRICS AND STATISTICS | en_US |
| dc.rights | openAccess | en_US |
| dc.source | 16th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2023), Book of Abstracts | en_US |
| dc.title | A new approach to Little's MCAR test | en_US |
| dc.type | conferenceObject | en_US |
| dc.citation.epage | 43 | en_US |
| dc.citation.spage | 43 | en_US |
| dc.type.version | publishedVersion | en_US |
| dc.identifier.url | https://www.cmstatistics.org/CMStatistics2023/docs/BoA.pdf?20231128014621 | - |
| item.cerifentitytype | Publications | - |
| item.fulltext | No Fulltext | - |
| item.languageiso639-1 | en | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.grantfulltext | none | - |
| item.openairetype | conferenceObject | - |
| Appears in Collections: | Radovi istraživača / Researchers’ publications | |
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