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You searched for +publisher:"Georgia Tech" +contributor:("Huo, Xiaoming"). Showing records 1 – 30 of 38 total matches.

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Georgia Tech

1. Cao, Yang. Poisson matrix completion and change-point detection.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

 Statistical signal processing and machine learning are very important in modern science and engineering. Many theories, methods and techniques are developed to help people extract and… (more)

Subjects/Keywords: Matrix completion; Sequential change-point detection; Robust change detection; Online learning

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APA (6th Edition):

Cao, Y. (2018). Poisson matrix completion and change-point detection. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60195

Chicago Manual of Style (16th Edition):

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/60195.

MLA Handbook (7th Edition):

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Web. 03 Apr 2020.

Vancouver:

Cao Y. Poisson matrix completion and change-point detection. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/60195.

Council of Science Editors:

Cao Y. Poisson matrix completion and change-point detection. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60195


Georgia Tech

2. Liu, Kun. EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

 Data science is changing our society and economy, and complicated data from heterogeneous sources is often collected in various industries such as finance, manufacturing, security,… (more)

Subjects/Keywords: Data science; Data integration; Change-point; CUSUM; Parallel computing; Quickest detection; Sensor networks; Distributed learning; Network connectivity; Sequential detection; Beta-regression; CDR; Generalized linear mixed model; ADNI database, Robust inference

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APA (6th Edition):

Liu, K. (2018). EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61160

Chicago Manual of Style (16th Edition):

Liu, Kun. “EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/61160.

MLA Handbook (7th Edition):

Liu, Kun. “EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS.” 2018. Web. 03 Apr 2020.

Vancouver:

Liu K. EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/61160.

Council of Science Editors:

Liu K. EFFICIENT DATA INTEGRATION TECHNIQUES IN SOME MODERN APPLICATIONS. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61160


Georgia Tech

3. Gong, Yun. Empirical likelihood and extremes.

Degree: PhD, Mathematics, 2012, Georgia Tech

 In 1988, Owen introduced empirical likelihood as a nonparametric method for constructing confidence intervals and regions. Since then, empirical likelihood has been studied extensively in… (more)

Subjects/Keywords: Diffusion processes; Nonparametric likelihood; GARCH; ROC curve; Value at Risk; Empirical likelihood; Extremal problems (Mathematics); Golden section; Calculus of variations; Bootstrap (Statistics); Mathematical statistics

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APA (6th Edition):

Gong, Y. (2012). Empirical likelihood and extremes. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43581

Chicago Manual of Style (16th Edition):

Gong, Yun. “Empirical likelihood and extremes.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/43581.

MLA Handbook (7th Edition):

Gong, Yun. “Empirical likelihood and extremes.” 2012. Web. 03 Apr 2020.

Vancouver:

Gong Y. Empirical likelihood and extremes. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/43581.

Council of Science Editors:

Gong Y. Empirical likelihood and extremes. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43581


Georgia Tech

4. Zhou, Ke. Extending low-rank matrix factorizations for emerging applications.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Low-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of… (more)

Subjects/Keywords: Matrix factorization; Collaborative filtering; Social network; Dimensional analysis Computer programs; Cluster analysis Data processing; Social networks

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APA (6th Edition):

Zhou, K. (2013). Extending low-rank matrix factorizations for emerging applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50230

Chicago Manual of Style (16th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/50230.

MLA Handbook (7th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Web. 03 Apr 2020.

Vancouver:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/50230.

Council of Science Editors:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50230


Georgia Tech

5. Sun, Xiuli. Firm-level human capital and innovation: evidence from China.

Degree: PhD, Economics, 2015, Georgia Tech

 This thesis examines firm innovation in China from firm-level human capital per- spective since resource-based theory and upper echelon theory reveal that the reason why… (more)

Subjects/Keywords: Human capital; Innovation; R&D

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APA (6th Edition):

Sun, X. (2015). Firm-level human capital and innovation: evidence from China. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53969

Chicago Manual of Style (16th Edition):

Sun, Xiuli. “Firm-level human capital and innovation: evidence from China.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/53969.

MLA Handbook (7th Edition):

Sun, Xiuli. “Firm-level human capital and innovation: evidence from China.” 2015. Web. 03 Apr 2020.

Vancouver:

Sun X. Firm-level human capital and innovation: evidence from China. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/53969.

Council of Science Editors:

Sun X. Firm-level human capital and innovation: evidence from China. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53969


Georgia Tech

6. Sim, Min Kyu. Empirical findings in asset price dynamics revealed by quantitative modelling.

Degree: PhD, Industrial and Systems Engineering, 2014, Georgia Tech

 This dissertation addresses the fundamental question of what factors drive equity prices and investigates the mechanisms through which the drivers influence the price dynamics. The… (more)

Subjects/Keywords: Quantitative finance; Asset pricing model; Factor model; Connectedness; High-frequency trading; Market impact; Price impact; Optimal order execution

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APA (6th Edition):

Sim, M. K. (2014). Empirical findings in asset price dynamics revealed by quantitative modelling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54302

Chicago Manual of Style (16th Edition):

Sim, Min Kyu. “Empirical findings in asset price dynamics revealed by quantitative modelling.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/54302.

MLA Handbook (7th Edition):

Sim, Min Kyu. “Empirical findings in asset price dynamics revealed by quantitative modelling.” 2014. Web. 03 Apr 2020.

Vancouver:

Sim MK. Empirical findings in asset price dynamics revealed by quantitative modelling. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/54302.

Council of Science Editors:

Sim MK. Empirical findings in asset price dynamics revealed by quantitative modelling. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/54302


Georgia Tech

7. Kim, Heeyoung. Statistical methods for function estimation and classification.

Degree: PhD, Industrial and Systems Engineering, 2011, Georgia Tech

 This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for fitting functions with varying roughness. In the first part of… (more)

Subjects/Keywords: Adaptive smoothing splines; Asymptotic optimality; Coordinate measuring machine; Wavelets; Classification; Mathematical statistics; Spline theory; Function spaces

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APA (6th Edition):

Kim, H. (2011). Statistical methods for function estimation and classification. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44806

Chicago Manual of Style (16th Edition):

Kim, Heeyoung. “Statistical methods for function estimation and classification.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/44806.

MLA Handbook (7th Edition):

Kim, Heeyoung. “Statistical methods for function estimation and classification.” 2011. Web. 03 Apr 2020.

Vancouver:

Kim H. Statistical methods for function estimation and classification. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/44806.

Council of Science Editors:

Kim H. Statistical methods for function estimation and classification. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/44806


Georgia Tech

8. Chen, Jiangning. Text-classification methods and the mathematical theory of Principal Components.

Degree: PhD, Mathematics, 2019, Georgia Tech

 This thesis studies three topics. First of all, in text classification, one may use Principal Components Analysis (PCA) as a dimension reduction technique, or with… (more)

Subjects/Keywords: Text-classification; NLP; PCA; Online PCA; Incremental scheme; Naive Bayes; Partial labeling; KL divergence

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APA (6th Edition):

Chen, J. (2019). Text-classification methods and the mathematical theory of Principal Components. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61686

Chicago Manual of Style (16th Edition):

Chen, Jiangning. “Text-classification methods and the mathematical theory of Principal Components.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/61686.

MLA Handbook (7th Edition):

Chen, Jiangning. “Text-classification methods and the mathematical theory of Principal Components.” 2019. Web. 03 Apr 2020.

Vancouver:

Chen J. Text-classification methods and the mathematical theory of Principal Components. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/61686.

Council of Science Editors:

Chen J. Text-classification methods and the mathematical theory of Principal Components. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61686

9. Feng, Chen. Feature learning and personalized screening techniques in healthcare.

Degree: PhD, Industrial and Systems Engineering, 2019, Georgia Tech

 Data science is playing an increasingly important role in improving public health. Data used for public health studies are in various types, including electronic health… (more)

Subjects/Keywords: Data science; Public health; Statistical; Machine learning; Visual impairment classification; Breast cancer diagnosis; Personalized screening; Sepsis; Prediction; Postoperative complications

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APA (6th Edition):

Feng, C. (2019). Feature learning and personalized screening techniques in healthcare. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61701

Chicago Manual of Style (16th Edition):

Feng, Chen. “Feature learning and personalized screening techniques in healthcare.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/61701.

MLA Handbook (7th Edition):

Feng, Chen. “Feature learning and personalized screening techniques in healthcare.” 2019. Web. 03 Apr 2020.

Vancouver:

Feng C. Feature learning and personalized screening techniques in healthcare. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/61701.

Council of Science Editors:

Feng C. Feature learning and personalized screening techniques in healthcare. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61701

10. Remenyi, Norbert. Contributions to Bayesian wavelet shrinkage.

Degree: PhD, Industrial and Systems Engineering, 2012, Georgia Tech

 This thesis provides contributions to research in Bayesian modeling and shrinkage in the wavelet domain. Wavelets are a powerful tool to describe phenomena rapidly changing… (more)

Subjects/Keywords: Bayes factor; Bayesian estimation; Bayesian infere; Wavelets (Mathematics); Bayesian statistical decision theory; Mathematical statistics

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APA (6th Edition):

Remenyi, N. (2012). Contributions to Bayesian wavelet shrinkage. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45898

Chicago Manual of Style (16th Edition):

Remenyi, Norbert. “Contributions to Bayesian wavelet shrinkage.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/45898.

MLA Handbook (7th Edition):

Remenyi, Norbert. “Contributions to Bayesian wavelet shrinkage.” 2012. Web. 03 Apr 2020.

Vancouver:

Remenyi N. Contributions to Bayesian wavelet shrinkage. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/45898.

Council of Science Editors:

Remenyi N. Contributions to Bayesian wavelet shrinkage. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45898

11. Valencia Arboleda, Carlos Felipe. Contributions to statistical learning and its applications in personalized medicine.

Degree: PhD, Industrial and Systems Engineering, 2013, Georgia Tech

 This dissertation, in general, is about finding stable solutions to statistical models with very large number of parameters and to analyze their asymptotic statistical properties.… (more)

Subjects/Keywords: Statistical learning; Regularization; Penalized estimation; Individualized treatment rule; Statistics; Multivariate analysis; Regression analysis

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APA (6th Edition):

Valencia Arboleda, C. F. (2013). Contributions to statistical learning and its applications in personalized medicine. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49143

Chicago Manual of Style (16th Edition):

Valencia Arboleda, Carlos Felipe. “Contributions to statistical learning and its applications in personalized medicine.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/49143.

MLA Handbook (7th Edition):

Valencia Arboleda, Carlos Felipe. “Contributions to statistical learning and its applications in personalized medicine.” 2013. Web. 03 Apr 2020.

Vancouver:

Valencia Arboleda CF. Contributions to statistical learning and its applications in personalized medicine. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/49143.

Council of Science Editors:

Valencia Arboleda CF. Contributions to statistical learning and its applications in personalized medicine. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49143

12. Shin, Jaemin. Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging.

Degree: PhD, Biomedical Engineering, 2012, Georgia Tech

 Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) contrast has become a widespread technique in brain research. The central challenge in… (more)

Subjects/Keywords: Functional connectivity; Resting state FMRI; Physiological noise; Magnetic resonance imaging; Neurosciences; Neurophysiology; Brain Localization of functions; Brain mapping

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APA (6th Edition):

Shin, J. (2012). Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44862

Chicago Manual of Style (16th Edition):

Shin, Jaemin. “Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/44862.

MLA Handbook (7th Edition):

Shin, Jaemin. “Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging.” 2012. Web. 03 Apr 2020.

Vancouver:

Shin J. Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/44862.

Council of Science Editors:

Shin J. Characterization and compensation of physiological fluctuations in functional magnetic resonance imaging. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44862

13. Wang, Ruodu. Some questions in risk management and high-dimensional data analysis.

Degree: PhD, Mathematics, 2012, Georgia Tech

 This thesis addresses three topics in the area of statistics and probability, with applications in risk management. First, for the testing problems in the high-dimensional… (more)

Subjects/Keywords: Empirical likelihood; Hypothesis testing; High-dimensional data; Risk measures; Copulas; Dimensional analysis; Analysis of covariance; Mathematical statistics; Data structures (Computer science); Risk management

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APA (6th Edition):

Wang, R. (2012). Some questions in risk management and high-dimensional data analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44706

Chicago Manual of Style (16th Edition):

Wang, Ruodu. “Some questions in risk management and high-dimensional data analysis.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/44706.

MLA Handbook (7th Edition):

Wang, Ruodu. “Some questions in risk management and high-dimensional data analysis.” 2012. Web. 03 Apr 2020.

Vancouver:

Wang R. Some questions in risk management and high-dimensional data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/44706.

Council of Science Editors:

Wang R. Some questions in risk management and high-dimensional data analysis. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44706

14. Feng, Huijun. Goodness-of-fit test and bilinear model.

Degree: PhD, Mathematics, 2012, Georgia Tech

 The Empirical Likelihood method (ELM) was introduced by A. B. Owen to test hypotheses in the early 1990s. It's a nonparametric method and uses the… (more)

Subjects/Keywords: Goodness-of-fit; ELM; Bilinear; Jackknife; Hypothesis test; Goodness-of-fit tests; Statistical hypothesis testing

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APA (6th Edition):

Feng, H. (2012). Goodness-of-fit test and bilinear model. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47722

Chicago Manual of Style (16th Edition):

Feng, Huijun. “Goodness-of-fit test and bilinear model.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/47722.

MLA Handbook (7th Edition):

Feng, Huijun. “Goodness-of-fit test and bilinear model.” 2012. Web. 03 Apr 2020.

Vancouver:

Feng H. Goodness-of-fit test and bilinear model. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/47722.

Council of Science Editors:

Feng H. Goodness-of-fit test and bilinear model. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/47722

15. Jeon, Seonghye. Bayesian data mining techniques in public health and biomedical applications.

Degree: PhD, Industrial and Systems Engineering, 2012, Georgia Tech

 The emerging research issues in evidence-based healthcare decision-making and explosion of comparative effectiveness research (CER) are evident proof of the effort to thoroughly incorporate the… (more)

Subjects/Keywords: Clinical studies meta-analysis; Public health; Wavelets; Classification; Bayesian; Data mining; Data mining; Bayesian statistical decision theory; Random walks (Mathematics); Markov processes; Monte Carlo method

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APA (6th Edition):

Jeon, S. (2012). Bayesian data mining techniques in public health and biomedical applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43712

Chicago Manual of Style (16th Edition):

Jeon, Seonghye. “Bayesian data mining techniques in public health and biomedical applications.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/43712.

MLA Handbook (7th Edition):

Jeon, Seonghye. “Bayesian data mining techniques in public health and biomedical applications.” 2012. Web. 03 Apr 2020.

Vancouver:

Jeon S. Bayesian data mining techniques in public health and biomedical applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/43712.

Council of Science Editors:

Jeon S. Bayesian data mining techniques in public health and biomedical applications. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43712

16. Huang, Cheng. Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing.

Degree: PhD, Industrial and Systems Engineering, 2017, Georgia Tech

 Parameter estimation and hypotheses testing are two fundamental problems in statistics. Many existing methods have been developed for the problems with moderate amount of data.… (more)

Subjects/Keywords: Distributed statistical inference; Test of Independence; Two-Sample test; One-step; Random projection

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APA (6th Edition):

Huang, C. (2017). Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60120

Chicago Manual of Style (16th Edition):

Huang, Cheng. “Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/60120.

MLA Handbook (7th Edition):

Huang, Cheng. “Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing.” 2017. Web. 03 Apr 2020.

Vancouver:

Huang C. Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/60120.

Council of Science Editors:

Huang C. Some computationally efficient methods in statistics and their applications in parameter estimation and hypotheses testing. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60120

17. Han, Zhi. Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

 This dissertation is dedicated to study the modeling of drift control in foreign exchange reserves management and design the fast algorithm of statistical inference with… (more)

Subjects/Keywords: Stochastic control; Foreign exchange reserve; Drift control; Verification theorem; Partial distance covariance; High dimensional data; Feature screening

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APA (6th Edition):

Han, Z. (2015). Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54401

Chicago Manual of Style (16th Edition):

Han, Zhi. “Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/54401.

MLA Handbook (7th Edition):

Han, Zhi. “Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data.” 2015. Web. 03 Apr 2020.

Vancouver:

Han Z. Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/54401.

Council of Science Editors:

Han Z. Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54401


Georgia Tech

18. Lee, Joongsup. New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles.

Degree: PhD, Industrial and Systems Engineering, 2011, Georgia Tech

 In this thesis, we first investigate the use of automated variance estimators in distribution-free statistical process control (SPC) charts for univariate autocorrelated processes. We introduce… (more)

Subjects/Keywords: SPC; Brownian motion; Wavelets; Profiles; Variance estimation; CUSUM; Process control Statistical methods; Quality control; Process control

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APA (6th Edition):

Lee, J. (2011). New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42711

Chicago Manual of Style (16th Edition):

Lee, Joongsup. “New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/42711.

MLA Handbook (7th Edition):

Lee, Joongsup. “New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles.” 2011. Web. 03 Apr 2020.

Vancouver:

Lee J. New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/42711.

Council of Science Editors:

Lee J. New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42711


Georgia Tech

19. Reynolds, Sue Bath. Statistical estimation and changepoint detection methods in public health surveillance.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

 This thesis focuses on assessing and improving statistical methods implemented in two areas of public health research. The first topic involves estimation of national influenza-associated… (more)

Subjects/Keywords: Statistical estimation; Changepoint detection

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APA (6th Edition):

Reynolds, S. B. (2015). Statistical estimation and changepoint detection methods in public health surveillance. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54906

Chicago Manual of Style (16th Edition):

Reynolds, Sue Bath. “Statistical estimation and changepoint detection methods in public health surveillance.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/54906.

MLA Handbook (7th Edition):

Reynolds, Sue Bath. “Statistical estimation and changepoint detection methods in public health surveillance.” 2015. Web. 03 Apr 2020.

Vancouver:

Reynolds SB. Statistical estimation and changepoint detection methods in public health surveillance. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/54906.

Council of Science Editors:

Reynolds SB. Statistical estimation and changepoint detection methods in public health surveillance. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54906

20. Sezer, Osman Gokhan. Data-driven transform optimization for next generation multimedia applications.

Degree: PhD, Electrical and Computer Engineering, 2011, Georgia Tech

 The objective of this thesis is to formulate a generic dictionary learning method with the guiding principle that states: Efficient representations lead to efficient estimations.… (more)

Subjects/Keywords: Compression; Image; DCT; Video; Risk minimization; Oracle risk; Sparse orthonormal transforms; Dictionary learning; Denoising; Multimedia communications; Signal processing; Image processing; Algorithms

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APA (6th Edition):

Sezer, O. G. (2011). Data-driven transform optimization for next generation multimedia applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42765

Chicago Manual of Style (16th Edition):

Sezer, Osman Gokhan. “Data-driven transform optimization for next generation multimedia applications.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/42765.

MLA Handbook (7th Edition):

Sezer, Osman Gokhan. “Data-driven transform optimization for next generation multimedia applications.” 2011. Web. 03 Apr 2020.

Vancouver:

Sezer OG. Data-driven transform optimization for next generation multimedia applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/42765.

Council of Science Editors:

Sezer OG. Data-driven transform optimization for next generation multimedia applications. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42765

21. Lu, Nan. Normally elliptic singular perturbation problems: local invariant manifolds and applications.

Degree: PhD, Mathematics, 2011, Georgia Tech

 In this thesis, we study the normally elliptic singular perturbation problems including both finite and infinite dimensional cases, which could also be non-autonomous. In particular,… (more)

Subjects/Keywords: Dynamical system; Invariant manifold; Homoclinic orbit; Perturbation (Mathematics); Singular perturbations (Mathematics)

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APA (6th Edition):

Lu, N. (2011). Normally elliptic singular perturbation problems: local invariant manifolds and applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/41090

Chicago Manual of Style (16th Edition):

Lu, Nan. “Normally elliptic singular perturbation problems: local invariant manifolds and applications.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/41090.

MLA Handbook (7th Edition):

Lu, Nan. “Normally elliptic singular perturbation problems: local invariant manifolds and applications.” 2011. Web. 03 Apr 2020.

Vancouver:

Lu N. Normally elliptic singular perturbation problems: local invariant manifolds and applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/41090.

Council of Science Editors:

Lu N. Normally elliptic singular perturbation problems: local invariant manifolds and applications. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/41090

22. Park, Soyoun. Penalized method based on representatives and nonparametric analysis of gap data.

Degree: PhD, Industrial and Systems Engineering, 2010, Georgia Tech

 When there are a large number of predictors and few observations, building a regression model to explain the behavior of a response variable such as… (more)

Subjects/Keywords: Survival function; Gap data; Representatives; Penalized method; Regression analysis; Correlation (Statistics); Simulation methods

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APA (6th Edition):

Park, S. (2010). Penalized method based on representatives and nonparametric analysis of gap data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37307

Chicago Manual of Style (16th Edition):

Park, Soyoun. “Penalized method based on representatives and nonparametric analysis of gap data.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/37307.

MLA Handbook (7th Edition):

Park, Soyoun. “Penalized method based on representatives and nonparametric analysis of gap data.” 2010. Web. 03 Apr 2020.

Vancouver:

Park S. Penalized method based on representatives and nonparametric analysis of gap data. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/37307.

Council of Science Editors:

Park S. Penalized method based on representatives and nonparametric analysis of gap data. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37307

23. Choi, Ki Sueng. Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging.

Degree: PhD, Biomedical Engineering (Joint GT/Emory Department), 2013, Georgia Tech

 Diffusion tensor imaging (DTI) is a noninvasive MRI technique used to assess white matter (WM) integrity, fiber orientation, and structural connectivity (SC) using water diffusion… (more)

Subjects/Keywords: Depression; Diffusion tensor imaging; Deep brain stimulation; White matter; Connectivity; Depression, Mental; Affective disorders; Brain stimulation; Neurobiology

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APA (6th Edition):

Choi, K. S. (2013). Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50353

Chicago Manual of Style (16th Edition):

Choi, Ki Sueng. “Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/50353.

MLA Handbook (7th Edition):

Choi, Ki Sueng. “Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging.” 2013. Web. 03 Apr 2020.

Vancouver:

Choi KS. Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/50353.

Council of Science Editors:

Choi KS. Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50353

24. Huang, Wei. Surfacelet-based heterogeneous materials modeling.

Degree: PhD, Mechanical Engineering, 2014, Georgia Tech

 The application of heterogeneous materials has become common in modern product design such as composites and porous media. Computational design tools for such materials, with… (more)

Subjects/Keywords: Surfacelet; Heterogeneous materials; Materials design; Materials modeling; Computer-aided engineering

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APA (6th Edition):

Huang, W. (2014). Surfacelet-based heterogeneous materials modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52333

Chicago Manual of Style (16th Edition):

Huang, Wei. “Surfacelet-based heterogeneous materials modeling.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/52333.

MLA Handbook (7th Edition):

Huang, Wei. “Surfacelet-based heterogeneous materials modeling.” 2014. Web. 03 Apr 2020.

Vancouver:

Huang W. Surfacelet-based heterogeneous materials modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/52333.

Council of Science Editors:

Huang W. Surfacelet-based heterogeneous materials modeling. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52333

25. Ni, Kai. The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection.

Degree: PhD, Mathematics, 2013, Georgia Tech

 This thesis is devoted to the detectability of an inhomogeneous region possibly embedded in a noisy environment. It presents models and algorithms using the theory… (more)

Subjects/Keywords: Filamentary detection; Longest significant chain; Asymptotically powerful test; Image detection; Image analysis; Image processing Statistical methods

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APA (6th Edition):

Ni, K. (2013). The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47683

Chicago Manual of Style (16th Edition):

Ni, Kai. “The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/47683.

MLA Handbook (7th Edition):

Ni, Kai. “The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection.” 2013. Web. 03 Apr 2020.

Vancouver:

Ni K. The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/47683.

Council of Science Editors:

Ni K. The asymptotic rate of the length of the longest significant chain with good continuation in Bernoulli net and its applications in filamentary detection. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47683

26. Lu, Yibiao. Statistical methods with application to machine learning and artificial intelligence.

Degree: PhD, Industrial and Systems Engineering, 2012, Georgia Tech

 This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization… (more)

Subjects/Keywords: Manifold learning; Laplacian-based regularization; Support vector machine; M-A*; M-LPA*; Electricity spikes; Boosting trees; Machine learning; Artificial intelligence; Estimation theory; Approximation theory; Smoothing (Numerical analysis)

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APA (6th Edition):

Lu, Y. (2012). Statistical methods with application to machine learning and artificial intelligence. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44730

Chicago Manual of Style (16th Edition):

Lu, Yibiao. “Statistical methods with application to machine learning and artificial intelligence.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/44730.

MLA Handbook (7th Edition):

Lu, Yibiao. “Statistical methods with application to machine learning and artificial intelligence.” 2012. Web. 03 Apr 2020.

Vancouver:

Lu Y. Statistical methods with application to machine learning and artificial intelligence. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/44730.

Council of Science Editors:

Lu Y. Statistical methods with application to machine learning and artificial intelligence. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44730

27. Jeong, Namin. A surfacelet-based method for constructing geometric models of microstructure.

Degree: PhD, Mechanical Engineering, 2015, Georgia Tech

 Integration of material composition, microstructure, and mechanical properties with geometry information enables many product development activities, including design, analysis, and manufacturing. To address such needs,… (more)

Subjects/Keywords: CAD; Surfacelet; Image processing; Radon transform; Structure-property relationship

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APA (6th Edition):

Jeong, N. (2015). A surfacelet-based method for constructing geometric models of microstructure. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54438

Chicago Manual of Style (16th Edition):

Jeong, Namin. “A surfacelet-based method for constructing geometric models of microstructure.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/54438.

MLA Handbook (7th Edition):

Jeong, Namin. “A surfacelet-based method for constructing geometric models of microstructure.” 2015. Web. 03 Apr 2020.

Vancouver:

Jeong N. A surfacelet-based method for constructing geometric models of microstructure. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/54438.

Council of Science Editors:

Jeong N. A surfacelet-based method for constructing geometric models of microstructure. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54438

28. Wang, Huizhu. Statistical selection and wavelet-based profile monitoring.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

 This thesis consists of two topics: statistical selection and profile monitoring. Statistical selection is related to ranking and selection in simulation and profile monitoring is… (more)

Subjects/Keywords: Ranking and selection; Wavelet-based statistical process control

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APA (6th Edition):

Wang, H. (2015). Statistical selection and wavelet-based profile monitoring. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53546

Chicago Manual of Style (16th Edition):

Wang, Huizhu. “Statistical selection and wavelet-based profile monitoring.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/53546.

MLA Handbook (7th Edition):

Wang, Huizhu. “Statistical selection and wavelet-based profile monitoring.” 2015. Web. 03 Apr 2020.

Vancouver:

Wang H. Statistical selection and wavelet-based profile monitoring. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/53546.

Council of Science Editors:

Wang H. Statistical selection and wavelet-based profile monitoring. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53546


Georgia Tech

29. Lavrik, Ilya A. Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis.

Degree: PhD, Industrial and Systems Engineering, 2005, Georgia Tech

 Given the recent popularity and clear evidence of wide applicability of wavelets, this thesis is devoted to several statistical applications of Wavelet transforms. Statistical multiscale… (more)

Subjects/Keywords: Nanoscale; Shrinkage; Classification; Wavelets

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APA (6th Edition):

Lavrik, I. A. (2005). Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/10424

Chicago Manual of Style (16th Edition):

Lavrik, Ilya A. “Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis.” 2005. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/10424.

MLA Handbook (7th Edition):

Lavrik, Ilya A. “Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis.” 2005. Web. 03 Apr 2020.

Vancouver:

Lavrik IA. Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2005. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/10424.

Council of Science Editors:

Lavrik IA. Novel Wavelet-Based Statistical Methods with Applications in Classification, Shrinkage, and Nano-Scale Image Analysis. [Doctoral Dissertation]. Georgia Tech; 2005. Available from: http://hdl.handle.net/1853/10424


Georgia Tech

30. Kim, Hyungjoon. Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding.

Degree: PhD, Electrical and Computer Engineering, 2007, Georgia Tech

 The primary objective of this thesis is to provide a low-complexity rate-distortion optimal coding mode selection method in digital video encoding. To achieve optimal compression… (more)

Subjects/Keywords: Mode selection; MPEG; Video coding

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APA (6th Edition):

Kim, H. (2007). Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/14513

Chicago Manual of Style (16th Edition):

Kim, Hyungjoon. “Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding.” 2007. Doctoral Dissertation, Georgia Tech. Accessed April 03, 2020. http://hdl.handle.net/1853/14513.

MLA Handbook (7th Edition):

Kim, Hyungjoon. “Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding.” 2007. Web. 03 Apr 2020.

Vancouver:

Kim H. Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2020 Apr 03]. Available from: http://hdl.handle.net/1853/14513.

Council of Science Editors:

Kim H. Low-Complexity Mode Selection for Rate-Distortion Optimal Video Coding. [Doctoral Dissertation]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/14513

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