標題: 具冪次轉換,隨機效應及AR(g)相關之成長曲線模型的參數估計與預測
Parameter Estimation and Future Value Prediction of Growth Curve Model with Power Transformation, Random Effects and General AR(g) Dependence
作者: 龔自良
Tz-Liang Kueng
李昭勝
J. C. Lee
統計學研究所
關鍵字: 近似;貝氏方法;長期資料;最大概似;馬可夫蒙地卡羅法;實際資料;模擬資料;Approximation;Bayesian;Longitudinal data;Maximum likelihood;Markov Chain Monte Carlo;Real data;Simulated data
公開日期: 2000
摘要: 在本篇論文中,我們著眼於以貝氏方法及最大概似法兩種 觀點來分析㆒具有幂次轉換,隨機效應及 AR(g)相關之廣義成 長曲線模型。所含蓋的統計推論除了參數的估計之外,尚包括 有對未觀測值的預測。另外,實際資料及模擬資料的分析比較 在文中均有詳細的討論。
In this paper, we devote ourselves to a generalized growth curve model with power transformation, random effects and AR($g$) dependence via Bayesian and Maximum-Likelihood (ML) approaches. Inferences on the parameters as well as the future value are discussed. Some numerical results with real and simulated data are also given.\\ {\bf Key words}: Approximation, Bayesian, Longitudinal data, Maximum likelihood, Markov Chain Monte Carlo, Real data, Simulated data.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT890337014
http://hdl.handle.net/11536/66765
顯示於類別:畢業論文