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姓名 藍佩琳(Pei-Lin Lan) 查詢紙本館藏 畢業系所 統計研究所 論文名稱 推論成對設計的篩檢預測值之新概似函數方法
(A new likelihood approach to inference about predictive values of diagnostic tests in paired designs)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 我們提出新的概似函數方法，來分析成對設計的資料，主要目的在比較兩個篩檢之陽性預測值或是兩個陰性預測值。直觀地，比較陽性預測值時僅需兩個篩檢結果為陽性的病患，反之，比較陰性預測值時僅需兩個篩檢結果為陰性的病患。現今大多數以多項式模型為基礎的方法，在模型中包含多餘的參數，導致推導出的統計量較為複雜。本文所介紹的新方法，只使用了最少量的感興趣參數。而我們的強韌分數檢定統計量與Kosinski (2013) 所提出的加權廣義分數統計量是完全相同的。 摘要(英) We propose a new likelihood approach to comparing two positive predictive values (PPVs) or two negative predictive values (NPVs) in paired designs. Intuitively, one only needs patients with two positive screening test results for PPVs comparison, and those with two negative screening test results for contrasting NPVs. evertheless, current existing methods rely on the multinomial model that includes superfluous parameters unnecessary for specific comparisons. This practice results in complex statistics formulas. We introduce a novel approach that fits the intuition by including a minimum number of parameters of interest and show that our robust test statistic is identical to the weighted generalized score
test statistic proposed by Kosinski (2013).關鍵字(中) ★ 強韌概似函數
★ 篩檢檢定
★ 陽性預測值
★ 陰性預測值關鍵字(英) ★ Robust likelihood
★ Diagnostic test
★ Positive predictive value
★ Negative predictive value論文目次 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 強韌概似函數 ...................................................................................................... 4
第三章 二元負二項模型及二元伯努力模型 .................................................................. 7
3.1 二元負二項 (Bivariate Negative Binomial) ...................................................... 7
3.2 二元伯努力 (Bivariate Bernoulli) ...................................................................... 8
第四章 兩個二元負二項模型之強韌化 ........................................................................ 11
4.1 參數之最大概似估計量 .................................................................................... 12
4.2 過離散參數不影響平均數之估計 .................................................................... 14
第五章 兩個伯努力模型之強韌化 ................................................................................ 17
5.1 參數之最大概似估計量 .................................................................................... 17
5.2 修正項A 之計算 ................................................................................................ 19
5.3 修正項B 之計算 ................................................................................................ 20
5.4 參數 之強韌Wald 統計量 .............................................................................. 25
5.5 參數 之強韌分數統計量 .............................................................................. 26
第六章 模擬研究 ............................................................................................................ 28
6.1 資料生成方法 .................................................................................................... 28
6.2 模擬結果 ............................................................................................................ 30
第七章 實例分析 ............................................................................................................ 65
第八章 結論 .................................................................................................................... 68
參考文獻 .......................................................................................................................... 69
附錄 ................................................................................................................................. 71參考文獻 [1] Dai, B., Ding, S. and Wahba, G. (2013). Multivariate bernoulli distribution. Bernoulli, 19,
1465-1483.
[2] Leisch, F., Weingessel, A. and Hornik, K. (1998). On the generation of correlated
artificial binary data, Working Paper Series 13, Vienna University of Economics.
Avaliable at http://www.wu-wien.ac. at/am.
[3] Kosinski, A. S. (2013). A weighted generalized score statistic for comparison of
predictive values of diagnostic tests. Statistics in Medicine, 32, 964-977.
[4] Lawrence, J. and Marion, R. (1991). A method for generating high-dimensional
Multivariate Binary Variates, The American Statistician, 45, 302-304.
[5] Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear
models. Biometrika, 73, 1, 13-22.
[6] Royall, R. M. and Tsou, T. S. (2003). Interpreting statistical evidence by using imperfect
models: robust adjusted likelihood functions. Journal of the Royal Statistical Society,
Series B, 65, 391-404.
[7] Tsou, T. S. and Chen, C. H. (2008). Comparing several means of dependent populations
of count-A parametric robust approach. Statistics in Medicine, 27, 2576-2585.
[8] Tsou, T. S. (2015). Robust likelihood inference for multivariate correlated count data.
Computational Statistics (to appear).指導教授 鄒宗山(Tsung-Shan Tsou) 審核日期 2015-7-21 推文 plurk
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