 |
|
|
|
以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：18 、訪客IP：18.223.210.20
姓名 洪執中(Chih-Chung Hung) 查詢紙本館藏 畢業系所 統計研究所 論文名稱 兩計數母體平均數比較之強韌樣本數計算 相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 ( 永不開放)
摘要(中) 本文探討在平行實驗設計下之過離散計數型資料的樣本數計算問題。我們使用經適當修正後，具強韌性的負二項概似函數，發展出一套計算樣本數的方式，此強韌有母數方法在不需知道資料真正分配的情形下亦能提供正確的樣本數。因此，以此強韌有母數方法分析平行實驗設計下之過離散的計數資料與計算所需樣本數是較佳的選擇。
此外我們也以Zhu & Lakkis (2013)的概念為基礎，推得另一套求取所需樣本數的計
算方式，並將此方法所得之結果與我們提出的方法算的樣本數做比較。摘要(英) This thesis proposes a way of calculating sample size that is suitable for general count data and over-dispersed count data in parallel designs. This robust sample size calculation task is accomplished by employing the negative binomial as the working model with a proper adjustment. We use various data configurations in simulation studies to demonstrate the merit of our new approach for calculating sample size. Contrasts are also made with the sample size method proposed by Zhu & Lakkis (2014). 關鍵字(中) ★ 強韌概似函數
★ 負二項模型
★ 樣本數
★ 平行實驗設計
★ 過離散關鍵字(英) ★ Robust likelihood
★ Negative binomial model
★ Sample size
★ Parallel design
★ Over-dispersion論文目次 目錄
摘要……………………………………………………………………………………………..i
Abstract………………………………………………………………………………………...ii
致謝辭......................................................................................................................................iii
目錄…………………………………………………………………………………………....iv
表目錄………………………………………………………………………………………....vi
第一章 緒論…………………………………………………………………………………...1
第二章 文獻回顧.......................................................................................................................4
2.1 平行實驗設計與母體假設……………………………………………………………4
2.2 負二項分配……………………………………………………………………………4
2.3 資料假設與負二項回歸模型…………………………………………………………5
2.4 樣本數公式……………………………………………………………………………6
2.5 樣本數公式的三種型態……………………………………………………………..12
2.5.1 方法1：取決於對照組………………………………………………………12
2.5.2 方法2：取決於真實值………………………………………………………13
2.5.3 方法3：取決於最大概似估計值……………………………………………13
2.6 所有試驗個體之受試時間不等下的樣本數計算…………………………………..16
2.7 α 與 β的最大概似估計量…………………………………………………………..18
2.7.1 所有試驗個體之受試時長相等………………………………………………18
v
2.7.2 所有試驗個體之受試時間不盡相等…………………………………………19
第三章 研究方法…………………………………………………………………………….21
3.1 強韌化的變異數……………………………………………………………………...21
3.2 理論驗證……………………………………………………………………………...26
3.3 強韌化的樣本數公式………………………………………………………………...27
3.4 所有試驗個體之受試時間不等下的樣本數計算…………………………………...28
第四章 模擬研究…………………………………………………………………………….30
4.1 基本性質檢驗………………………………………………………………………...30
4.2 混和型負二項資料…………………………………………………………………...39
4.3 非負二項的過離散資計數型資料…………………………………………………...57
4.4 固定樣本數下的比較………………………………………………………………...63
4.5 所有受試個體之受試時間不盡相等的資料………………………………………...66
第五章 結論………………………………………………………………………………….77
參考文獻……………………………………………………………………………………...78參考文獻 [1] Zhu, H., and Lakkis, H. (2014). Sample size calculation for comparing two negative
binomial rates. Statistics in Medicine, 33, 376-387.
[2] Keene, O.N., Jones, M.R., Lane, P.W. and Anderson, J. (2007). Analysis of exacerbation
rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN
study. Pharmaceutical Statistics, 6, 89-97.
[3] Rettiganti, M. and Nagaraja, H.N. (2012). Power analyses for negative binomial models
with application to multiple sclerosis clinical trials. Journal of Biopharmaceutical
Statistics, 22, 237-259.
[4] Lloyd-Smith, J.O. (2007). Maximum likelihood estimation of the negative binomial
dispersion parameter for highly overdispersed data, with applications to infectious
diseases. PLoS ONE, 2, e180.
[5] Royall, R., and Tsou, T.S. (2003). Interpreting statistical evidence by using imperfect
models: robust adjusted likelihood functions. Journal of the Royal Statistical Society:
Series B (Statistical Methodology), 65, 391-404.
[6] Metcalfe, C. and Thompson, S.G. (2006). The importance of varying the event generation
process in simulation studies of statistical methods for recurrent events. Statistics in
Medicine, 25, 165-179.
[7] Sormani, M. P., Miller, D. H., Comi, G., Barkhof, F., Rovaris, M., Bruzzi, P., and Filippi,
79
M. (2001). Clinical trials of multiple sclerosis monitored with enhanced MRI: new sample
size calculations based on large data sets. Journal of Neurology, Neurosurgery and
Psychiatry, 70, 494-499.
[8] Aban, I.B., Cutter, G.R. and Mavinga, N. (2009). Inferences and power analysis
concerning two negative binomial distributions with an application to MRI lesion counts
data. Computational Statistics and Data Analysis, 53, 820-833.
[9] Consul, P.C. (1989). Generalized Poisson distribution, Marcel Dekker, Inc.
[10] 侯宜君 (2014), 一個普世的分析交叉實驗個數資料的有母數強韌法。(國立中央大
學統計研究所碩士論文)。指導教授 鄒宗山(Tsung-Shan Tsou) 審核日期 2016-7-13 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit