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Queens University

1. Liu, Shifang. Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials .

Degree: Mathematics and Statistics, 2011, Queens University

URL: http://hdl.handle.net/1974/6758

Treatment–covariate interaction is often used in clinical trials to assess the homogeneity of treatment effects over these subgroups defined by a baseline covariate, which is frequently conducted after primary analysis including all patients is completed. When the endpoint is the time to an event, as in the cancer clinical trials, the Cox proportional hazard model with an interaction term has been used exclusively to test the significance of treatment-covariate interaction in oncology literature. But the proportional hazards assumption may not be satisfied by the data from clinical trials. Although there are several procedures proposed in statistical literature to assess the interaction based on a nonparametric measure of interaction or nonparametric models, some of these procedures do not take into the account of the nature of the data well, while some are very complicated which may have limited their applications in practice. In this thesis, a non-parametric procedure based on the smoothed estimate of Patel–Hoel measure is first derived to test the interaction between the treatment and a binary covariate with censored data. The theoretical distribution of the test statistic of the proposed procedure is derived. The proposed procedure is also evaluated through Monte-Carlo simulations and applications to data from a cancer clinical trial. Jackknifed versions of two test statistics based on nonparametric models are then derived by simplifying these test statistics and applying the jackknife method to estimate their variances. These jackknifed tests are also compared with the smoothed test and other related tests.

Subjects/Keywords: Jackknife estimate ; Monte-Carlo simulation ; nonparametric measure of interaction ; Kernel smooth

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Liu, S. (2011). Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/6758

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liu, Shifang. “Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials .” 2011. Thesis, Queens University. Accessed January 19, 2021. http://hdl.handle.net/1974/6758.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Shifang. “Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials .” 2011. Web. 19 Jan 2021.

Vancouver:

Liu S. Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials . [Internet] [Thesis]. Queens University; 2011. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1974/6758.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu S. Statistical Methods for Testing Treatment-Covariate Interactions in Cancer Clinical Trials . [Thesis]. Queens University; 2011. Available from: http://hdl.handle.net/1974/6758

Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

2. Černík, Tomáš. Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/56606

Bachelor thesis deals with detection of unusual events (anomalies) in available temporal data. Theoretical part describes existing techniques and algorithms used to detect outliers. There are also introduced meteorological data that are after that used for experimental verification of implemented detection algorithms. Second part, practical one, describes design and implementation of application and algorithms. Algorithms are also tested in search for point, contextual and collective anomalies.
*Advisors/Committee Members: Zendulka, Jaroslav (advisor), Bartík, Vladimír (referee).*

Subjects/Keywords: Anomálie; detekční algoritmy; dovolání znalostí z dat; Perl; temporální data; algoritmus kNN; JackKnife Estimate; Outliers; Anomaly; detection algorithms; data mining; Perl; temporal data; kNN algorithm; JackKnife Estimate

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Černík, T. (2019). Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56606

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Černík, Tomáš. “Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data.” 2019. Thesis, Brno University of Technology. Accessed January 19, 2021. http://hdl.handle.net/11012/56606.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Černík, Tomáš. “Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data.” 2019. Web. 19 Jan 2021.

Vancouver:

Černík T. Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/11012/56606.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Černík T. Detekce neobvyklých událostí v temporálních datech: Detection of Unusual Events in Temporal Data. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/56606

Not specified: Masters Thesis or Doctoral Dissertation

Purdue University

3. Li, Lingnan. Maximum empirical likelihood estimation in U-statistics based general estimating equations.

Degree: PhD, Mathematics, 2016, Purdue University

URL: https://docs.lib.purdue.edu/open_access_dissertations/796

In the first part of this thesis, we study maximum empirical likelihood estimates (MELE's) in U-statistics based general estimating equations (UGEE's). Our technical maneuver is the jackknife empirical likelihood (JEL) approach. We give the local uniform asymptotic normality condition for the log-JEL for UGEE's. We derive the estimating equations for finding MELE's and provide their asymptotic normality. We obtain easy MELE's which have less computational burden than the usual MELE's and can be easily implemented using existing software. We investigate the use of side information of the data to improve efficiency. We exhibit that the MELE's are fully efficient, and the asymptotic variance of a MELE will not increase as the number of UGEE's increases. We give several important examples and demonstrate that efficient estimates of moment based distribution characteristics in the presence of side information can be obtained using JEL for U-statistics.
In the second part, we propose several JEL goodness-of-fit tests for spherical symmetry, rotational symmetry, antipodal symmetry, coordinatewise symmetry and exchangeability. We employ the jackknife empirical likelihood for vector U-statistics to incorporate side information. We use estimated constraint functions and allow the number of constraints and the dimension to grow with the sample size so that these tests can be used to test hypotheses for high dimensional symmetries. We demonstrate that these tests are distribution free and asymptotically chisquare distributed. We conduct extensive simulations to evaluate the performance of these tests.
*Advisors/Committee Members: Hanxiang Peng, Hanxiang Peng, Benzion Boukai, Guang Cheng, Wei Zheng.*

Subjects/Keywords: Pure sciences; Jackknife pseudo value; Maximum empirical likelihood estimate; U-statistics; U-statistics based general estimating equations; Mathematics; Statistics and Probability

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Li, L. (2016). Maximum empirical likelihood estimation in U-statistics based general estimating equations. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/796

Chicago Manual of Style (16^{th} Edition):

Li, Lingnan. “Maximum empirical likelihood estimation in U-statistics based general estimating equations.” 2016. Doctoral Dissertation, Purdue University. Accessed January 19, 2021. https://docs.lib.purdue.edu/open_access_dissertations/796.

MLA Handbook (7^{th} Edition):

Li, Lingnan. “Maximum empirical likelihood estimation in U-statistics based general estimating equations.” 2016. Web. 19 Jan 2021.

Vancouver:

Li L. Maximum empirical likelihood estimation in U-statistics based general estimating equations. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2021 Jan 19]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/796.

Council of Science Editors:

Li L. Maximum empirical likelihood estimation in U-statistics based general estimating equations. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/796