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Dissertations / Theses on the topic 'AREA UNDER THE ROC-CURVE (AUC)'

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Published: 5 June 2025

Last updated: 2 August 2025

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1

Zheng, Shimin. "The ROC Curve and the Area under the Curve (AUC)." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/139.

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Sun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.

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Thesis (M.S.)--Georgia State University, 2008.<br>Title from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).

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Zhu, Yao-Wei. "LIMITED SAMPLING STRATEGIES FOR FACILE DETERMINATION OF THE AREA UNDER THE CURVE OF ANTI-CANCER AGENTS, PACLITAXEL AND SU5416." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin984580545.

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Xu, Ping. "Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4261.

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Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asym

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Mistry, Pritesh. "A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14440.

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This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicit

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Glisson, Wesley J., Courtney J. Conway, Christopher P. Nadeau, and Kathi L. Borgmann. "Habitat models to predict wetland bird occupancy influenced by scale, anthropogenic disturbance, and imperfect detection." WILEY, 2017. http://hdl.handle.net/10150/625200.

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Understanding species-habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation

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池田, 充., Takeo Ishigaki, Mitsuru Ikeda, 一信山内, and Kazunobu Yamauchi. "Relationship between Brier score and area under the binormal ROC curve." Elsevier, 2002. http://hdl.handle.net/2237/5310.

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8

Galadima, Hadiza I. "Controlling for Confounding when Association is Quantified by Area Under the ROC Curve." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3905.

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In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio.

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9

Liu, Hua. "ASYMPTOTIC PROPERTIES OF PARTIAL AREAS UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE WITH APPLICATIONS IN MICROARRAY EXPERIMENTS." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_diss/463.

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Receiver operating characteristic (ROC) curves are widely used in medical decision making. It was recognized in the last decade that only a specific region of the ROC curve is of clinical interest, which can be summarized by the partial area under the ROC curve (partial AUC). Early statistical methods for evaluating partial AUC assume that the data are from a specified underlying distribution. Nonparametric estimators of the partial AUC emerged recently, but there are theoretical issues to be addressed. In this dissertation, we propose two new nonparametric statistics, partially integrated ROC

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10

Huang, Xin. "Some Topics in Roc Curves Analysis." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/3.

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The receiver operating characteristic (ROC) curves is a popular tool for evaluating continuous diagnostic tests. The traditional definition of ROC curves incorporates implicitly the idea of "hard" thresholding, which also results in the empirical curves being step functions. The first topic is to introduce a novel definition of soft ROC curves, which incorporates the idea of "soft" thresholding. The softness of a soft ROC curve is controlled by a regularization parameter that can be selected suitably by a cross-validation procedure. A byproduct of the soft ROC curves is that the corresponding

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11

Kutálek, David. "Statistická analýza ROC křivek." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-229036.

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The ROC (Receiver Operating Characteristic) curve is a projection of two different cumulative distribution functions F0 and F1. On axis are values 1-F0(c) and 1-F1(c). The c-parameter is a real number. This curve is useful to check quality of discriminant rule which classify an object to one of two classes. The criterion is a size of an area under the curve. To solve real problems we use point and interval estimation of ROC curves and statistical hypothesis tests about ROC curves.

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12

Dunning, Allison. "Comparing Bootstrap and Jackknife Variance Estimation Methods for Area Under the ROC Curve Using One-Stage Cluster Survey Data." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1849.

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The purpose of this research is to examine the bootstrap and jackknife as methods for estimating the variance of the AUC from a study using a complex sampling design and to determine which characteristics of the sampling design effects this estimation. Data from a one-stage cluster sampling design of 10 clusters was examined. Factors included three true AUCs (.60, .75, and .90), three prevalence levels (50/50, 70/30, 90/10) (non-disease/disease), and finally three number of clusters sampled (2, 5, or 7). A simulated sample was constructed for each of the 27 combinations of AUC, prevalence

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Li, Yi. "A Generalization of AUC to an Ordered Multi-Class Diagnosis and Application to Longitudinal Data Analysis on Intellectual Outcome in Pediatric Brain-Tumor Patients." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_diss/1.

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Receiver operating characteristic (ROC) curves have been widely used in evaluation of the goodness of the diagnostic method in many study fields, such as disease diagnosis in medicine. The area under the ROC curve (AUC) naturally became one of the most used variables in gauging the goodness of the diagnosis (Mossman, Somoza 1991). Since medical diagnosis often is not dichotomous, the ROC curve and AUC need to be generalized to a multi-dimensional case. The generalization of AUC to multi-class case has been studied by many researchers in the past decade. Most recently, Nakas & Yiannoutsos (2

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14

Tang, Tian. "Infrared Spectroscopy in Combination with Advanced Statistical Methods for Distinguishing Viral Infected Biological Cells." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/59.

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Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences

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15

Luo, Shan. "Advanced Statistical Methodologies in Determining the Observation Time to Discriminate Viruses Using FTIR." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/86.

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Fourier transform infrared (FTIR) spectroscopy, one method of electromagnetic radiation for detecting specific cellular molecular structure, can be used to discriminate different types of cells. The objective is to find the minimum time (choice among 2 hour, 4 hour and 6 hour) to record FTIR readings such that different viruses can be discriminated. A new method is adopted for the datasets. Briefly, inner differences are created as the control group, and Wilcoxon Signed Rank Test is used as the first selecting variable procedure in order to prepare the next stage of discrimination. In the seco

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16

Takáo, Thales Baliero. "Classificação de dados cinéticos da inicialização da marcha utilizando redes neurais artificiais e máquinas de vetores de suporte." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/5586.

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Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-20T12:55:18Z No. of bitstreams: 2 Dissertação - Thales Baliero Takáo - 2015.pdf: 2798998 bytes, checksum: f90a7c928230875abd5873753316f766 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)<br>Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-20T12:56:48Z (GMT) No. of bitstreams: 2 Dissertação - Thales Baliero Takáo - 2015.pdf: 2798998 bytes, checksum: f90a7c928230875abd5873753316f766 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)<

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17

Lin, Shu-Chuan. "Robust estimation for spatial models and the skill test for disease diagnosis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26681.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Lu, Jye-Chyi; Committee Co-Chair: Kvam, Paul; Committee Member: Mei, Yajun; Committee Member: Serban, Nicoleta; Committee Member: Vidakovic, Brani. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Ou, Ju-Chi, and 歐茹琪. "The estimation of the area under multivariate ROC curve." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/10016159564156825199.

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碩士<br>國立成功大學<br>統計學系碩博士班<br>91<br>A statistical tool which is popular for describing the accuracy of diagnostic is the receive operating characteristic (ROC) curve. There has been an increased use of ROC curve for assessing the effectiveness of continuous diagnostic markers in distinguishing between diseased and healthy individuals. McClish (1989) recognized that the area under the ROC curve(AUROC) is a global measure of a test''s accuracy because it includes the entire range of false-positive rates from 0.0 to 1.0. In fact, it is possible that several diagnostic markers are used to detect

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19

Chen, Yu-Heng, and 陳禹衡. "Classification based on the area under ROC curve using genetic algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/20258668507253799889.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>104<br>Binary classification based on a feature vector is a long-standing problem. Linear discriminant analysis proposed by Fisher finds a linear combination of features such that the ratio of the variation between class to the variation within class is maximized. Similar to the linear discriminant analysis, we propose a linear combination of features such that the area under the receiver operating characteristic curve is maximized. The optimal linear combination is approximated by the genetic algorithm. Empirical results demonstrate substancial improvement on the cl

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20

Tau, Han-Wei, and 陶漢威. "Non-inferiority tests based on the partial area under ROC curve." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/25014094570157144564.

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碩士<br>國立中央大學<br>統計研究所<br>100<br>In this paper, we consider testing the non-inferiority of two medical diagnostic methods in a case-control study where each subject receiving the two different diagnostics produces correlated paired measurements. Note that it occurs often in practice that the marginal distributions of the measurements are right-skewed. Therefore, we first apply the power transformation to the paired data so that they would behave like the bivariate normal data. One parametric non-inferiority test is then implemented based on the transformed data. On the other hand, we suggest an

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21

Hsiao, Li-Cheng, and 蕭力誠. "Meta Analysis Method for Area under ROC Curve: Application to Glaucoma Diagnosis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87122944735184404646.

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碩士<br>淡江大學<br>數學學系碩士班<br>99<br>Meta-analysis is a quantitative weighted average method to combine the results of related but independent studies (usually drawn from the published literatures) and synthesize summaries and conclusions which may be used to evaluate the therapeutic effects and/or plain new study accordingly. The commonly used meta-analyses, dependent on the characteristic of data, are: the relative risk, odds ratio, and rates difference for binary data and effect size and meta-regression for normally distributed data. Meta-analysis for area under ROC curve (AUC), a commonly used m

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22

Ho, Chung-Yuan, and 何仲遠. "Meta Regression Analysis Method for Area under ROC Curve: Application to Glaucoma Diagnosis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/92443545856026228649.

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碩士<br>淡江大學<br>數學學系碩士班<br>99<br>Meta-analysis is a quantitative weighted average method to combine the results of related but independent studies (usually drawn from the published literatures) and synthesize summaries and conclusions which may be used to evaluate the therapeutic effects and/or plain new study accordingly. The commonly used meta-analyses, dependent on the characteristic of data, are: the relative risk, odds ratio, and rates difference for binary data and effect size and meta-regression for normally distributed data. Meta-analysis for area under ROC curve (AUC), a commonly used m

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23

LEE, MING-HSUEH, and 李明學. "Performance of tests based on the area under the ROC curve for multireader diagnostic data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/539yps.

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碩士<br>國立臺北大學<br>統計學系<br>106<br>The ROC (Receiver operating characteristic) curve is a measure for evaluating diagnostic reagents. The AUC (area under the ROC curve) is one of the most popular indices to evaluate the accuracy of the diagnostic reagents. X-ray films or MRI scans are often used to evaluate the status of cancer patients. Nevertheless, the outcome of these instruments requires professional physicians to determine. To ensure the accuracy of the instrument and avoid the possibility of reading bias from physicians, the outcome of these instruments is evaluated by several professional

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24

Chu, Su Peter. "Statistical Geocomputing: Spatial Outlier Detection in Precision Agriculture." Thesis, 2011. http://hdl.handle.net/10012/6347.

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The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region

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25

Lemos, Catarina Isabel Ferreira Miranda. "Seleção de genes diferencialmente expressos baseada em metodologia ROC (Receiver Operating Characteristic)." Master's thesis, 2017. http://hdl.handle.net/1822/56110.

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Dissertação de mestrado em Bioinformática<br>A análise da expressão genética é essencial para uma identificação da função dos genes e para a identificação destes quando relacionados com doenças. Para a realização de um estudo em larga escala de mudanças na expressão genética é necessário encontrar um método que o faça com precisão e exatidão. Desta forma, foi aqui incluída, uma análise pela tecnologia de microarrays, uma ferramenta importante no diagnóstico de doenças. A execução de um método que identificasse genes com regulação negativa e positiva e genes diferencialmente expressos sim

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