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You searched for `+publisher:"University of Illinois – Chicago" +contributor:("Yang, Min")`

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University of Illinois – Chicago

1. Cheng, Qianshun. Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/21995

My thesis includes two major parts which are described as follows.
The first part develops a new powerful algorithm for multiple-constrained optimal design problems. Experiments with multiple objectives form a staple diet of modern scientific research. Deriving optimal designs with multiple objectives is a long-standing challenging problem with only a few tools available. The few existing approaches cannot provide a fully satisfactory solution in general: either the computation is very expensive, or a satisfactory solution is not guaranteed.
A novel algorithm is proposed to address this literature gap. We prove the convergence of this algorithm, and show in various examples that the new algorithm can derive the true solutions with high speed.
The second part is develops an information-based optimal subdata selection strategy, which can efficiently pick out subsample of fixed size from massive data set with the logistic regression model. Advances in computes technology have enabled an exponential growth in data collection and the size of data sets. For the extraordinary large data sets, proven statistical methods are no longer applicable due to computational limitations. A critical step in Big Data analysis is data reduction. In this thesis, we investigate the sampling approach of selecting subsets under the logistic regression model. For random sampling approaches, it is shown that the information contained in the subdata is limited by the size of the subset.
A novel framework of selecting subsets is proposed. The information contained in the subdata based on the new framework increases as size of full data increases. The respective performances of the proposed approaches, along with some of the widely-applied existing methods, are compared under various criteria based on extensive simulation studies.
*Advisors/Committee Members: Yang, Min (advisor).*

Subjects/Keywords: Constrained Optimal Design; IBOSS

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

APA (6^{th} Edition):

Cheng, Q. (2017). Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21995

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):

Cheng, Qianshun. “Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model.” 2017. Thesis, University of Illinois – Chicago. Accessed February 17, 2019. http://hdl.handle.net/10027/21995.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Cheng, Qianshun. “Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model.” 2017. Web. 17 Feb 2019.

Vancouver:

Cheng Q. Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10027/21995.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheng Q. Novel Algorithm for Constrained Optimal Design and Information-based Subdata Selection for Logistic Model. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21995

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago

2. Syring, Nicholas A. Gibbs Posterior Distributions: New Theory and Applications.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/22219

Bayesian inference is, by far, the most well-known statistical method for updating beliefs
about a population feature of interest in light of new data. Current beliefs, characterized by a
probability distribution called a prior, are updated by combining with data, which is modeled
as a random draw from another probability distribution. The Bayesian framework, therefore,
depends heavily on the choices of model distributions for prior and data, and it is the latter
that is of particular concern in this dissertation. Often, as will be shown in various examples, it is particularly difficult to make a good choice of data model: a bad choice may lead to misspecification and inconsistency of the posterior distribution, or may introduce nuisance parameters, increasing computational burden and complicating the choice of prior. Some particular statistical problems that may give Bayesians pause are classification and quantile regression. In these two problems a mathematical function called a loss function serves as the natural connection between the data and the population feature. Statistical inference based on loss functions can avoid having to specify a probability model for the data and parameter, which may be incorrect. Bayes' Theorem cannot reconcile a posterior update using anything other than a probability model for data, so alternative methods are needed, besides Bayes, in order to take advantage of loss functions in these types of problems.
Gibbs posteriors, like Bayes posteriors, incorporate prior information and new data via an updating formula. However, the Gibbs posterior does not require modeling the data with a probability model as in Bayes; rather, data and parameter may be linked by a more general function, like the loss functions mentioned above. The Gibbs approach offers many potential benefits including robustness when the data distribution is not known and a natural avoidance of nuisance parameters, but Gibbs posteriors are not common throughout statistics literature. In an effort to raise awareness of Gibbs posteriors, this dissertation both develops new theoretical foundations and presents numerous examples highlighting the usefulness of Gibbs posteriors in statistical applications.
Two new asymptotic results for Gibbs posteriors are contributed. The main conclusion of the first result is that Gibbs posteriors have similar asymptotic behavior to a class of statistical estimators called M-estimators in a wide range of problems. The main advantage of the Gibbs posterior, then, is its ability to incorporate prior information. The second result extends results for Bayesian posteriors to Gibbs posteriors in a statistics problems where the population feature of interest is a set with a smooth boundary.
Additionally, two main applications are considered, one in medical statistics and one in image analysis. The first application concerns the minimum clinically important difference (MCID), a parameter designed to indicate whether the effect of a medical treatment is practically signi cant. Modeling for the…
*Advisors/Committee Members: Yang, Min (advisor).*

Subjects/Keywords: Model-free statistics; posterior convergence rate

Record Details Similar Records

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

APA (6^{th} Edition):

Syring, N. A. (2017). Gibbs Posterior Distributions: New Theory and Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22219

Not specified: Masters Thesis or Doctoral Dissertation

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

Syring, Nicholas A. “Gibbs Posterior Distributions: New Theory and Applications.” 2017. Thesis, University of Illinois – Chicago. Accessed February 17, 2019. http://hdl.handle.net/10027/22219.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Syring, Nicholas A. “Gibbs Posterior Distributions: New Theory and Applications.” 2017. Web. 17 Feb 2019.

Vancouver:

Syring NA. Gibbs Posterior Distributions: New Theory and Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10027/22219.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Syring NA. Gibbs Posterior Distributions: New Theory and Applications. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22219

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago

3. Tian, Tian. Optimal Design Theory in Early-Phase Dose-Finding Problems.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/22097

Phase I clinical trials concerns the estimation of the MTD (maximum tolerated dose), the dose level corresponding to the target toxicity rate. A great deal of methods have been proposed to address the MTD estimation problem, among which the CRM (continual reassessment method) stands out due to its simplicity and outstanding performance. We extend the classic CRM by incorporating the idea of optimal design theory. We denote this new approach the OD-CRM, which indicates that this strategy is developed within the CRM framework, and coupled with the optimal design theory.
Then we move on to a more practical problem encountered in the oncology clinical studies, the late-onset toxicities. We adopt the weighting mechanism discussed in Cheung and Chappell (2000) which essentially assigns each toxicity response to a weight that depends on the patient's enrollment time and the observed data.
We also offer a general dose- finding algorithm based on the OWEA (optimal weight exchange algorithm, Yang, Biedermann, and Tang, 2013), to explore the performance of the OD-CRM under a broader clinical trial setup.
*Advisors/Committee Members: Yang, Min (advisor), Hedayat, Samad (advisor).*

Subjects/Keywords: Optimal design; dose-finding; clinical trial; OWEA; time-to-event

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Tian, T. (2017). Optimal Design Theory in Early-Phase Dose-Finding Problems. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22097

Not specified: Masters Thesis or Doctoral Dissertation

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

Tian, Tian. “Optimal Design Theory in Early-Phase Dose-Finding Problems.” 2017. Thesis, University of Illinois – Chicago. Accessed February 17, 2019. http://hdl.handle.net/10027/22097.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Tian, Tian. “Optimal Design Theory in Early-Phase Dose-Finding Problems.” 2017. Web. 17 Feb 2019.

Vancouver:

Tian T. Optimal Design Theory in Early-Phase Dose-Finding Problems. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10027/22097.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tian T. Optimal Design Theory in Early-Phase Dose-Finding Problems. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22097

Not specified: Masters Thesis or Doctoral Dissertation