:::
Journal paper
篇名(中) 潛在變項選擇模型結構方程模型之最大概似估計
篇名(英)Maximum Likelihood Estimation of Structural Equation Models with Latent Variable Selection Model
論文屬性研究論文
作者姓名(中)鄭中平;翁儷禎
作者姓名(英)Chung-Ping Cheng; Li-Jen Weng
頁碼 005-027
Abstract本研究目的在以MCEM 算則(Monte Carlo expectationmaximization algorithm) 進行潛在變項影響資料遺漏時結構方程模 型的參數估計。結構方程模型分析常需實徵資料驗證研究者的假設模 型,資料發生遺漏是真收集過程經常遭遇的情形。遺漏可能與外顯變 頂有關,但亦可能與潛在變項有關。Muthen、Kaplan 與Hollis (1987) 描述了外顯變項或潛在變項影響遺漏與否的遺漏機制模型,並發現多 數情形下為不可忽略遺漏,現行之遺漏值處理法未必適用。因此,本 研究針對Muthén 等人之遺漏機制模型發展結構方程模型參數估計 方法,並以實例比較真與常用遺漏值處理法的差異,初步發現本研究 建議方法在潛在變項影響資料遺漏情形下表現最佳。
摘要(英)The Monte Carlo expectation-maximization algorithm was proposed for parameter estimation with latent variable selection model in structural equation modeling. Latent variables are allowed to influence data missingness in latent variable selection model. This missing mechanism in most cases is not missing at random. The missing data treatment methods available at present therefore may not be applicable and new development is called for. An empirical example of latent variable selection model was presented. The results indicated that the proposed method yielded satisfactory parameter estimates.
Keyword非隨機遺漏;選擇模型;結構方程模型;最大概似法;潛在變項
關鍵字(英)nonignorable missingness; selection model; structural equation modeling; maximum likelihood estimation; latent variable
Attached File 全文下載 Adobe PDF

Calendar

« August 2020»
MonTueWedThuFriSatSun
     0102
03040506070809
10111213141516
17181920212223
24252627282930
31
cron web_use_log