Hierarchical Missing Data and Multivariate Behrens–Fisher Problem
| Autor: | Jianqi Yu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Article Subject Generalization General Mathematics Interval estimation 02 engineering and technology Pivotal quantity Missing data 01 natural sciences Hierarchical database model Multivariate Behrens–Fisher problem 010104 statistics & probability Monotone polygon 0202 electrical engineering electronic engineering information engineering QA1-939 020201 artificial intelligence & image processing 0101 mathematics Algorithm Mathematics |
| Zdroj: | Journal of Mathematics, Vol 2021 (2021) |
| ISSN: | 2314-4629 |
| DOI: | 10.1155/2021/8837044 |
| Popis: | This article firstly defines hierarchical data missing pattern, which is a generalization of monotone data missing pattern. Then multivariate Behrens–Fisher problem with hierarchical missing data is considered to illustrate that how ideas in dealing with monotone missing data can be extended to deal with hierarchical missing pattern. A pivotal quantity similar to the Hotelling T 2 is presented, and the moment matching method is used to derive its approximate distribution which is for testing and interval estimation. The precision of the approximation is illustrated through Monte Carlo data simulation. The results indicate that the approximate method is very satisfactory even for moderately small samples. |
| Databáze: | OpenAIRE |
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