ロード中...
Normal Theory GLS Estimator for Missing Data: An Application to Item-Level Missing Data and a Comparison to Two-Stage ML
Structural equation models (SEMs) can be estimated using a variety of methods. For complete normally distributed data, two asymptotically efficient estimation methods exist: maximum likelihood (ML) and generalized least squares (GLS). With incomplete normally distributed data, an extension of ML cal...
保存先:
| 出版年: | Front Psychol |
|---|---|
| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Frontiers Media S.A.
2017
|
| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5439014/ https://ncbi.nlm.nih.gov/pubmed/28588523 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fpsyg.2017.00767 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|