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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45205完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江金倉 | |
| dc.contributor.author | Chih-Heng Chiu | en |
| dc.contributor.author | 邱志恆 | zh_TW |
| dc.date.accessioned | 2021-06-15T04:08:47Z | - |
| dc.date.available | 2011-02-11 | |
| dc.date.copyright | 2010-02-11 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-02-03 | |
| dc.identifier.citation | [1] Bickel, P. J. and Freedman, D. A. (1981). Some asymptotic theory for the bootstrap. Annals of Statistics. 9, 1196-1217.
[2] DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988). Comparing the area under tow or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44, 837-845. [3] Efron, B. (1979). Bootstrap methods: another look at the Jackknife. Annals of Statistics. 7, 1-26. [4] Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics. 19, 293-325. [5] Hosmer D. W. and Lemeshow S. (2000). Applied logistic regression. Wiley, New York. [6] Ichimura, H. (1993). Semiparametric least squares (SLS) and weighted SLS estimation of single-index models. Journal of Econometrics. 58, 71-120. [7] Ma, S. and Huang, J. (2007). Combining multiple markers for classification using ROC. Biometrics. 63, 751-757. [8] Maouloud, S. M. O. (2008). Some uniform large deviation results in nonparametric function estimation. Journal of Nonparametric Statistics. 20, 129-152. [9] McIntosh, M. S. and Pepe, M. S. (2002). Combining Several Screening Tests: Optimality of the Risk Score. Biometrics. 58, 657-664. [10] Mokkadem, A., Pelletier, M., and Thiam, B. (2008). Large and moderate deviations principles for kernel estimators of a multivariate regression. Mathematical Methods of Statistics. 17, 146-172. [11] Pepe, M. S., Cai, T., and Longton, G. (2006). Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve. Biometrics. 62, 221-229. [12] Qin, G. and Zhou, X. H. (2006). Empirical likelihood inference for the area under the ROC curve. Biometrics. 62, 613-622. [13] Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B. 53, 683-690. [14] Signorini, D. F. and Jones, M. C. (2004). Kernel estimators for univariate binary regression. Journal of the American Statistical Association. 99, 119-126. [15] Scott, A. J. and Wild, C. J. (1997). Fitting regression models to case-control data by maximum likelihood. Biometrika. 84, 57-71. [16] Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., and Johannes, R. S. (1988). Using the adap learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care, 261 ? V265. IEEE Computer Society Press. [17] Yalc¸ın, M. and Yıldırım, T. (2003). Karaci˘gerbozukluklarının yapay sinir a˘gları ile tes¸hisi. In Biyomedikal M¨uhendisli˘gi Ulusal Toplantısı (BIYOMUT 2003), Istanbul, T¨urkiye, 293-297. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45205 | - |
| dc.description.abstract | 概似函數指標相對應之ROC曲線已廣為所知為所有轉換指標相對應曲線中之最高。我們進一步獲知，在不同抽樣結構下，擁有相同之概似函數所衍生之條件機率函數指標亦擁有最高ROC曲線。此條件機率函數指標在發展理論及實務上有其方便性及解釋性。由於此最佳轉換指標為未知函數而以非參數方法估計，因此傳統ROC及AUC分析所用方法可能衍生不適性。針對此問題，我們提出相關的統計推論，並進一步由模擬實驗來驗證所提出之方法。最後我們將所提出之一系列方法應用在印地安那婦女糖尿病及肝病研究資料上。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-15T04:08:47Z (GMT). No. of bitstreams: 1 ntu-99-R96221019-1.pdf: 558474 bytes, checksum: 8e6eaa629c508bbfda521a66633a6bf0 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 1 Introduction 1
2 Estimation and Inferences 4 2.1 Estimation . . . . . . . . . . . . . . . . . . . 4 2.2 Bandwidth Selection . . . . . . . . . . . . . . . 6 2.3 Confidence Intervals . . . . . . . . . . . . . . .6 2.4 Testing Procedures . . . . . . . . . . . . . . . .8 3 Asymptotic Properties 10 4 Monte Carlo Simulations 19 4.1 Simulation I - Univariate Marker . . . . . . . . 19 4.2 Simulation II - Multiple Markers . . . . . . . . 28 5 Data Examples 34 5.1 Application to a Study of Pima-Indian Diabetes . 34 5.2 Application to a Study of Liver Disorder . . . . 36 6 Concluding Remarks 41 Bibliography 43 | |
| dc.language.iso | en | |
| dc.subject | 曲線下面積 | zh_TW |
| dc.subject | 漸近變異 | zh_TW |
| dc.subject | 真陽性率 | zh_TW |
| dc.subject | ROC 曲線 | zh_TW |
| dc.subject | 帶寬 | zh_TW |
| dc.subject | 自助重取法 | zh_TW |
| dc.subject | 偽陽性率 | zh_TW |
| dc.subject | U-統計量 | zh_TW |
| dc.subject | 非參數估計式 | zh_TW |
| dc.subject | bootstrap | en |
| dc.subject | U-statistic | en |
| dc.subject | true positive rate | en |
| dc.subject | receiver operating characteristic curve | en |
| dc.subject | optimal marker | en |
| dc.subject | nonparametric estimator | en |
| dc.subject | bandwidth | en |
| dc.subject | false positive rate | en |
| dc.subject | area under curve | en |
| dc.subject | asymptotic variance | en |
| dc.title | 最佳指標相對應ROC曲線之統計推論 | zh_TW |
| dc.title | Statistical Inferences for the Receiver Operating Characteristic Curve Analysis of An Optimal Marker | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳宏,周若珍 | |
| dc.subject.keyword | 曲線下面積,漸近變異,帶寬,自助重取法,偽陽性率,非參數估計式,ROC 曲線,真陽性率,U-統計量, | zh_TW |
| dc.subject.keyword | area under curve,asymptotic variance,bandwidth,bootstrap,false positive rate,nonparametric estimator,optimal marker,receiver operating characteristic curve,true positive rate,U-statistic, | en |
| dc.relation.page | 45 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-02-04 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
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