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| 研究生： |
陳大昌 Chen, Ta-Chang |
|---|---|
| 論文名稱： |
隨機過程在線性迴歸ME模型下的估計問題 Estimates in linear regression ME models for processes with uncorrelated increments |
| 指導教授： |
吳鐵肩
Wu, Tiee-Jian |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 統計學系 Department of Statistics |
| 論文出版年： | 2002 |
| 畢業學年度： | 90 |
| 語文別： | 英文 |
| 論文頁數： | 26 |
| 中文關鍵詞： | Gauss-Markov 定理 、收斂 、隨機過程 、ME模型 、連續性線性迴歸 |
| 外文關鍵詞： | Gauss-Markov theorem, measurement error model, consistency, stochastic processes with uncorrelated increment, Continuous-time linear regression |
| 相關次數： | 點閱：112 下載：1 |
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近年來，在許多工業、生物科學及醫學上的資料是以精密儀器收集。因此我們所得到的資料是一段與時間有關的連續型曲線，在這一篇論文中，我們將提出一個方法估計隨機過程在線性迴歸ME模型下的參數。我們也建立在 Berkson model 下的 Gauss-Markov 定理。此外，也提出一些關於參數的收斂性質。
In recent years, a lot of industrial, biological, and medical processes are continuously monitored by instruments under the control of microprocessors. Thus, our data is a set of curves de ned on certain time interval. This paper presents a method of estimating parameters in the ME (measurement error ) model for a stochastic process with uncorrelated increments.Based on the sample path(s) of such process(es) the estimates of regression parameters are obtained. We establish a Gauss-Markov theorem for the proposed estimator in the multiple linear regression Berkson model. Furthermore, the consistency in q.m.(quadratic mean) of the proposed estimator is established.
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校內：立即公開校外：2002-08-22公開