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Dates: 2015 – 2019 University: University of Michigan  Language: English

You searched for subject:(Education Data processing ). Showing records 1 – 30 of 502 total matches.

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University of Michigan

1. Jha, Rahul Kumar. NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the… (more)

Subjects/Keywords: natural language processing; automatic summarization; scholarly data; Computer Science; Engineering

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APA (6th Edition):

Jha, R. K. (2015). NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113407

Chicago Manual of Style (16th Edition):

Jha, Rahul Kumar. “NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.” 2015. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/113407.

MLA Handbook (7th Edition):

Jha, Rahul Kumar. “NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.” 2015. Web. 19 Oct 2019.

Vancouver:

Jha RK. NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/113407.

Council of Science Editors:

Jha RK. NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113407

2. Woods, Adrienne. Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification.

Degree: PhD, Education & Psychology, 2018, University of Michigan

 This dissertation is comprised of three studies using restricted data from the ECLS-K:1998 to address the questions who is placed in special education? and what… (more)

Subjects/Keywords: special education; longitudinal data; child development; secondary data analysis; educational psychology; Education; Social Sciences

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Woods, A. (2018). Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/146053

Chicago Manual of Style (16th Edition):

Woods, Adrienne. “Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification.” 2018. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/146053.

MLA Handbook (7th Edition):

Woods, Adrienne. “Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification.” 2018. Web. 19 Oct 2019.

Vancouver:

Woods A. Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/146053.

Council of Science Editors:

Woods A. Who is Placed in Special Education? Assessing the Longitudinal Profiles, Academic Achievement, and Behavioral Adjustment of Students At-Risk for Special Education Identification. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/146053

3. Tandon, Prateek. Hardware Acceleration for Unstructured Big Data and Natural Language Processing.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 The confluence of the rapid growth in electronic data in recent years, and the renewed interest in domain-specific hardware accelerators presents exciting technical opportunities. Traditional… (more)

Subjects/Keywords: hardware accelerators; unstrtuctured big data; natural language processing; unstructured log processing; Computer Science; Engineering

processing the vast amounts of text data have been shown to be energy- and cost-inefficient. In… …unstructured big-data processing and natural language processing. The first accelerator, called HAWK… …selections and scans over in-memory text data. HAWK is designed with an objective of processing in… …motivated primarily by text log processing, general streaming data query processing has many of… …data sharding (i.e., breaking the dataset into independent parts for parallel processing… 

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APA (6th Edition):

Tandon, P. (2015). Hardware Acceleration for Unstructured Big Data and Natural Language Processing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/116712

Chicago Manual of Style (16th Edition):

Tandon, Prateek. “Hardware Acceleration for Unstructured Big Data and Natural Language Processing.” 2015. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/116712.

MLA Handbook (7th Edition):

Tandon, Prateek. “Hardware Acceleration for Unstructured Big Data and Natural Language Processing.” 2015. Web. 19 Oct 2019.

Vancouver:

Tandon P. Hardware Acceleration for Unstructured Big Data and Natural Language Processing. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/116712.

Council of Science Editors:

Tandon P. Hardware Acceleration for Unstructured Big Data and Natural Language Processing. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/116712

4. Tan, Yan Shuo. Some Algorithms and Paradigms for Big Data.

Degree: PhD, Mathematics, 2018, University of Michigan

 The reality of big data poses both opportunities and challenges to modern researchers. Its key features  – large sample sizes, high-dimensional feature spaces, and structural… (more)

Subjects/Keywords: big data; optimization; mathematical data science; machine learning; signal processing; Mathematics; Science

…Another way to get around the computational bottleneck is by pre-processing the data to make it… …viii ABSTRACT The reality of big data poses both opportunities and challenges to modern… …complexity – enforce new paradigms upon the creation of effective yet algorithmic efficient data… …complexity. ix CHAPTER 1 Introduction 1.1 Big Data We live in the age of big data. As early… …would all reach to the moon. Since then, the sheer quantity of data that we possess has only… 

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APA (6th Edition):

Tan, Y. S. (2018). Some Algorithms and Paradigms for Big Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145895

Chicago Manual of Style (16th Edition):

Tan, Yan Shuo. “Some Algorithms and Paradigms for Big Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/145895.

MLA Handbook (7th Edition):

Tan, Yan Shuo. “Some Algorithms and Paradigms for Big Data.” 2018. Web. 19 Oct 2019.

Vancouver:

Tan YS. Some Algorithms and Paradigms for Big Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/145895.

Council of Science Editors:

Tan YS. Some Algorithms and Paradigms for Big Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145895

5. Melville, Alexander. An Automatic System for Characterization and Detection of Ocular Noise.

Degree: MSin Engineering, Computer Science, College of Engineering and Computer Science, 2017, University of Michigan

 Eye blinks cause high amplitude noise in electroencephalograms (EEGs), the noise from these blinks causes interference in several very important frequency bands. The method detailed… (more)

Subjects/Keywords: Data Science; Signal Processing; Wearables; Data Mining; Biomedical; EEG; Computer Science

…this thesis has two major components: Automatic data annotation, and model training using… …features. In order to make the system as accurate as possible, the training data must consist of… …a large variety of features extracted from the input data. A theoretical ideal model for… …different categories of features from the input data, so that the feature space contains as… …in a very similar frequency band to real neurological data, so it's not possible to… 

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APA (6th Edition):

Melville, A. (2017). An Automatic System for Characterization and Detection of Ocular Noise. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/136624

Chicago Manual of Style (16th Edition):

Melville, Alexander. “An Automatic System for Characterization and Detection of Ocular Noise.” 2017. Masters Thesis, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/136624.

MLA Handbook (7th Edition):

Melville, Alexander. “An Automatic System for Characterization and Detection of Ocular Noise.” 2017. Web. 19 Oct 2019.

Vancouver:

Melville A. An Automatic System for Characterization and Detection of Ocular Noise. [Internet] [Masters thesis]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/136624.

Council of Science Editors:

Melville A. An Automatic System for Characterization and Detection of Ocular Noise. [Masters Thesis]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/136624

6. Park, Yongjoo. Fast Data Analytics by Learning.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 Today, we collect a large amount of data, and the volume of the data we collect is projected to grow faster than the growth of… (more)

Subjects/Keywords: big data analytics systems; database systems; approximate query processing; database learning; Computer Science; Engineering

…sufficient for real-time data analytics of big data. Approximate query processing (AQP)… …helps us avoid processing the same data repeatedly if a new query involves common (or… …processing • stream data processing In this dissertation, we focus on applying our two approaches… …amounts of raw data. Also, processing more queries should continuously enhance our knowledge of… …x28;i) significantly faster response times by processing smaller samples of the data… 

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APA (6th Edition):

Park, Y. (2017). Fast Data Analytics by Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138598

Chicago Manual of Style (16th Edition):

Park, Yongjoo. “Fast Data Analytics by Learning.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/138598.

MLA Handbook (7th Edition):

Park, Yongjoo. “Fast Data Analytics by Learning.” 2017. Web. 19 Oct 2019.

Vancouver:

Park Y. Fast Data Analytics by Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/138598.

Council of Science Editors:

Park Y. Fast Data Analytics by Learning. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138598


University of Michigan

7. Necamp, Timothy. Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education.

Degree: PhD, Statistics, 2019, University of Michigan

 Dynamic treatment regimes, also called adaptive interventions, guide sequential treatment decision-making in a variety of fields, including healthcare and education. Dynamic treatment regimes accommodate differences… (more)

Subjects/Keywords: statistics; experimental design; mental health; online education; sequential randomization; Medicine (General); Public Health; Statistics and Numeric Data; Health Sciences; Science; Social Sciences

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APA (6th Edition):

Necamp, T. (2019). Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151503

Chicago Manual of Style (16th Edition):

Necamp, Timothy. “Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151503.

MLA Handbook (7th Edition):

Necamp, Timothy. “Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education.” 2019. Web. 19 Oct 2019.

Vancouver:

Necamp T. Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151503.

Council of Science Editors:

Necamp T. Design and Analysis of Sequential Randomized Trials with Applications to Mental Health and Online Education. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151503


University of Michigan

8. Ko, Inah. Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models.

Degree: PhD, Educational Studies, 2019, University of Michigan

 This study proposes a way of organizing mathematical knowledge for teaching that permits to reveal its multidimensionality. Scholars concerned with teachers’ mathematical knowledge have traditionally… (more)

Subjects/Keywords: MKT; Instructional Situation; Task of Teaching; Calculation in Geometry; Solving Equations in Algebra; Doing Proofs; Education; Statistics and Numeric Data; Social Sciences

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APA (6th Edition):

Ko, I. (2019). Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151510

Chicago Manual of Style (16th Edition):

Ko, Inah. “Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151510.

MLA Handbook (7th Edition):

Ko, Inah. “Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models.” 2019. Web. 19 Oct 2019.

Vancouver:

Ko I. Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151510.

Council of Science Editors:

Ko I. Investigating the Dimensionality of Teachers' Mathematical Knowledge for Teaching Secondary Mathematics Using Item Factor Analyses and Diagnostic Classification Models. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151510


University of Michigan

9. Murray, Laura. Neural Mechanisms of Reward Processing in Antisocial Behavior.

Degree: PhD, Psychology, 2019, University of Michigan

 Antisocial Behavior (AB), is associated with persistence in risky, reward-driven behaviors despite severe potential consequences such as incarceration. Neuroimaging research has the potential to elucidate… (more)

Subjects/Keywords: Antisocial Behavior; Reward Processing; Loss Processing; Neuroimaging; Adolescence; Psychology; Social Sciences

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APA (6th Edition):

Murray, L. (2019). Neural Mechanisms of Reward Processing in Antisocial Behavior. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151511

Chicago Manual of Style (16th Edition):

Murray, Laura. “Neural Mechanisms of Reward Processing in Antisocial Behavior.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151511.

MLA Handbook (7th Edition):

Murray, Laura. “Neural Mechanisms of Reward Processing in Antisocial Behavior.” 2019. Web. 19 Oct 2019.

Vancouver:

Murray L. Neural Mechanisms of Reward Processing in Antisocial Behavior. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151511.

Council of Science Editors:

Murray L. Neural Mechanisms of Reward Processing in Antisocial Behavior. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151511


University of Michigan

10. Yang, Ye. Robust Methods for Estimating the Mean with Missing Data.

Degree: PhD, Biostatistics, 2015, University of Michigan

 Missing data are common in many empirical studies. In this dissertation, we explore robust methods to estimate the mean of an outcome variable subject to… (more)

Subjects/Keywords: missing data; doubly robust; Statistics and Numeric Data; Science

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APA (6th Edition):

Yang, Y. (2015). Robust Methods for Estimating the Mean with Missing Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113402

Chicago Manual of Style (16th Edition):

Yang, Ye. “Robust Methods for Estimating the Mean with Missing Data.” 2015. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/113402.

MLA Handbook (7th Edition):

Yang, Ye. “Robust Methods for Estimating the Mean with Missing Data.” 2015. Web. 19 Oct 2019.

Vancouver:

Yang Y. Robust Methods for Estimating the Mean with Missing Data. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/113402.

Council of Science Editors:

Yang Y. Robust Methods for Estimating the Mean with Missing Data. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113402


University of Michigan

11. Guo, Cui. Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data.

Degree: PhD, Biostatistics, 2019, University of Michigan

 As both clinical and cognitive neuroscience matures, the need for sophisticated neuroimaging analyses becomes more important. The use of imaging markers to predict clinical outcomes,… (more)

Subjects/Keywords: Bayesian Methods; Nueroimaging Data Analysis; Statistics and Numeric Data; Science

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APA (6th Edition):

Guo, C. (2019). Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151627

Chicago Manual of Style (16th Edition):

Guo, Cui. “Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151627.

MLA Handbook (7th Edition):

Guo, Cui. “Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data.” 2019. Web. 19 Oct 2019.

Vancouver:

Guo C. Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151627.

Council of Science Editors:

Guo C. Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151627


University of Michigan

12. Narisetty, Naveen Naidu. Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth.

Degree: PhD, Statistics, 2016, University of Michigan

 Big data of the modern era exhibit different types of complex structures. This dissertation addresses two important problems that arise in this context. Consider high-dimensional… (more)

Subjects/Keywords: High Dimensional Data, Bayesian Model Selection, Functional Data, Data Depth, Complex Data, Bayesian Computation, Skinny Gibbs, Gene Expression; Mathematics; Science (General); Statistics and Numeric Data; Science

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APA (6th Edition):

Narisetty, N. N. (2016). Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120686

Chicago Manual of Style (16th Edition):

Narisetty, Naveen Naidu. “Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth.” 2016. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/120686.

MLA Handbook (7th Edition):

Narisetty, Naveen Naidu. “Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth.” 2016. Web. 19 Oct 2019.

Vancouver:

Narisetty NN. Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/120686.

Council of Science Editors:

Narisetty NN. Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120686


University of Michigan

13. King, Benjamin Philip. Practical Natural Language Processing for Low-Resource Languages.

Degree: PhD, Computer Science and Engineering, 2015, University of Michigan

 As the Internet and World Wide Web have continued to gain widespread adoption, the linguistic diversity represented has also been growing. Simultaneously the field of… (more)

Subjects/Keywords: Natural Language Processing; Computer Science; Engineering

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APA (6th Edition):

King, B. P. (2015). Practical Natural Language Processing for Low-Resource Languages. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113373

Chicago Manual of Style (16th Edition):

King, Benjamin Philip. “Practical Natural Language Processing for Low-Resource Languages.” 2015. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/113373.

MLA Handbook (7th Edition):

King, Benjamin Philip. “Practical Natural Language Processing for Low-Resource Languages.” 2015. Web. 19 Oct 2019.

Vancouver:

King BP. Practical Natural Language Processing for Low-Resource Languages. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/113373.

Council of Science Editors:

King BP. Practical Natural Language Processing for Low-Resource Languages. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113373


University of Michigan

14. Ju, Xiaoen. Efficient Large-Scale Graph Processing.

Degree: PhD, Computer Science and Engineering, 2016, University of Michigan

 The abundance of large graphs and the high potential for insight extraction from them have fueled interest in large-scale graph processing systems. Despite significant enhancement… (more)

Subjects/Keywords: graph processing systems; Computer Science; Engineering

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APA (6th Edition):

Ju, X. (2016). Efficient Large-Scale Graph Processing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133378

Chicago Manual of Style (16th Edition):

Ju, Xiaoen. “Efficient Large-Scale Graph Processing.” 2016. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/133378.

MLA Handbook (7th Edition):

Ju, Xiaoen. “Efficient Large-Scale Graph Processing.” 2016. Web. 19 Oct 2019.

Vancouver:

Ju X. Efficient Large-Scale Graph Processing. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/133378.

Council of Science Editors:

Ju X. Efficient Large-Scale Graph Processing. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133378


University of Michigan

15. Zhang, Yilan. Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data.

Degree: PhD, Civil Engineering, 2016, University of Michigan

 Structural monitoring systems are an objective and quantitative-based management tool that have been developed to assist structure owners with their diagnostic and prognostic structural condition… (more)

Subjects/Keywords: data management; data-driven analytics; structural condition assessment; structural monitoring data; Civil and Environmental Engineering; Engineering

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APA (6th Edition):

Zhang, Y. (2016). Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/135857

Chicago Manual of Style (16th Edition):

Zhang, Yilan. “Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data.” 2016. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/135857.

MLA Handbook (7th Edition):

Zhang, Yilan. “Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data.” 2016. Web. 19 Oct 2019.

Vancouver:

Zhang Y. Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/135857.

Council of Science Editors:

Zhang Y. Scalable Data Management and Data-Driven Analytics for Structural Condition Assessment using Structural Monitoring Data. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/135857


University of Michigan

16. Cai, Jiarui. Computational Approaches for Estimating Life Cycle Inventory Data.

Degree: MS, Natural Resources and Environment, 2016, University of Michigan

Data gaps in life cycle inventory (LCI) are stumbling blocks for investigating the life cycle performance and impact of emerging technologies. It can be tedious,… (more)

Subjects/Keywords: life cycle assessment; link prediction; data estimation

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APA (6th Edition):

Cai, J. (2016). Computational Approaches for Estimating Life Cycle Inventory Data. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/134693

Chicago Manual of Style (16th Edition):

Cai, Jiarui. “Computational Approaches for Estimating Life Cycle Inventory Data.” 2016. Masters Thesis, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/134693.

MLA Handbook (7th Edition):

Cai, Jiarui. “Computational Approaches for Estimating Life Cycle Inventory Data.” 2016. Web. 19 Oct 2019.

Vancouver:

Cai J. Computational Approaches for Estimating Life Cycle Inventory Data. [Internet] [Masters thesis]. University of Michigan; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/134693.

Council of Science Editors:

Cai J. Computational Approaches for Estimating Life Cycle Inventory Data. [Masters Thesis]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/134693


University of Michigan

17. Kong, Andy. Computational Strategies for Proteogenomics Analyses.

Degree: PhD, Bioinformatics, 2017, University of Michigan

 Proteogenomics is an area of proteomics concerning the detection of novel peptides and peptide variants nominated by genomics and transcriptomics experiments. While the term primarily… (more)

Subjects/Keywords: proteogenomics; Statistics and Numeric Data; Science

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APA (6th Edition):

Kong, A. (2017). Computational Strategies for Proteogenomics Analyses. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138581

Chicago Manual of Style (16th Edition):

Kong, Andy. “Computational Strategies for Proteogenomics Analyses.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/138581.

MLA Handbook (7th Edition):

Kong, Andy. “Computational Strategies for Proteogenomics Analyses.” 2017. Web. 19 Oct 2019.

Vancouver:

Kong A. Computational Strategies for Proteogenomics Analyses. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/138581.

Council of Science Editors:

Kong A. Computational Strategies for Proteogenomics Analyses. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138581


University of Michigan

18. Qian, Cheng. Some Advances on Modeling High-Dimensional Data with Complex Structures.

Degree: PhD, Statistics, 2017, University of Michigan

 Recent advances in technology have created an abundance of high-dimensional data and made its analysis possible. These data require new, computationally efficient methodology and new… (more)

Subjects/Keywords: High-Dimensional; Statistics and Numeric Data; Science

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APA (6th Edition):

Qian, C. (2017). Some Advances on Modeling High-Dimensional Data with Complex Structures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140828

Chicago Manual of Style (16th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/140828.

MLA Handbook (7th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Web. 19 Oct 2019.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/140828.

Council of Science Editors:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140828


University of Michigan

19. Antenucci, Dolan. Maximizing Insight from Modern Economic Analysis.

Degree: PhD, Computer Science & Engineering, 2018, University of Michigan

 The last decade has seen a growing trend of economists exploring how to extract different economic insight from "big data" sources such as the Web.… (more)

Subjects/Keywords: economic big data analysis; Computer Science; Engineering

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APA (6th Edition):

Antenucci, D. (2018). Maximizing Insight from Modern Economic Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/144007

Chicago Manual of Style (16th Edition):

Antenucci, Dolan. “Maximizing Insight from Modern Economic Analysis.” 2018. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/144007.

MLA Handbook (7th Edition):

Antenucci, Dolan. “Maximizing Insight from Modern Economic Analysis.” 2018. Web. 19 Oct 2019.

Vancouver:

Antenucci D. Maximizing Insight from Modern Economic Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/144007.

Council of Science Editors:

Antenucci D. Maximizing Insight from Modern Economic Analysis. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/144007


University of Michigan

20. Katz-Samuels, Julian. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Michigan

 Many applications can be modeled as follows: an agent is given access to several distributions and she wishes to determine those that meet some pre-specified… (more)

Subjects/Keywords: Adaptive Data Collection; Computer Science; Engineering

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APA (6th Edition):

Katz-Samuels, J. (2019). Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151539

Chicago Manual of Style (16th Edition):

Katz-Samuels, Julian. “Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151539.

MLA Handbook (7th Edition):

Katz-Samuels, Julian. “Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria.” 2019. Web. 19 Oct 2019.

Vancouver:

Katz-Samuels J. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151539.

Council of Science Editors:

Katz-Samuels J. Problems in Pure Exploration Multi-Armed Bandits with Multi-Dimensional Feedback and Criteria. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151539


University of Michigan

21. Kim, Yura. Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging.

Degree: PhD, Statistics, 2019, University of Michigan

 Neuroimaging data on functional connections in the brain are frequently represented by weighted networks. These networks share the same set of labeled nodes corresponding to… (more)

Subjects/Keywords: Network; Statistics and Numeric Data; Science

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APA (6th Edition):

Kim, Y. (2019). Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151555

Chicago Manual of Style (16th Edition):

Kim, Yura. “Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151555.

MLA Handbook (7th Edition):

Kim, Yura. “Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging.” 2019. Web. 19 Oct 2019.

Vancouver:

Kim Y. Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151555.

Council of Science Editors:

Kim Y. Statistical Tools for Samples of Weighted Networks with Applications to Neuroimaging. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151555


University of Michigan

22. Hunt, Gregory. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.

Degree: PhD, Statistics, 2018, University of Michigan

 Transformations are an important aspect of data analysis. In this work we explore the impact of data transformation on the analysis of high-throughput -omics data.… (more)

Subjects/Keywords: Cell Type Deconvolution and Transformation of Microenvironment Microarray Data; Statistics and Numeric Data; Science

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APA (6th Edition):

Hunt, G. (2018). Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147575

Chicago Manual of Style (16th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/147575.

MLA Handbook (7th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Web. 19 Oct 2019.

Vancouver:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/147575.

Council of Science Editors:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147575


University of Michigan

23. Imbriano, Paul. Methods for Improving Efficiency of Planned Missing Data Designs.

Degree: PhD, Biostatistics, 2018, University of Michigan

 Any survey specifically constructed so that at least some variables are unobserved on a subset of participants is a planned missing data design, where missing… (more)

Subjects/Keywords: planned missing data; two-phase sampling; split questionnaire design; Statistics and Numeric Data; Science

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APA (6th Edition):

Imbriano, P. (2018). Methods for Improving Efficiency of Planned Missing Data Designs. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/144155

Chicago Manual of Style (16th Edition):

Imbriano, Paul. “Methods for Improving Efficiency of Planned Missing Data Designs.” 2018. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/144155.

MLA Handbook (7th Edition):

Imbriano, Paul. “Methods for Improving Efficiency of Planned Missing Data Designs.” 2018. Web. 19 Oct 2019.

Vancouver:

Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/144155.

Council of Science Editors:

Imbriano P. Methods for Improving Efficiency of Planned Missing Data Designs. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/144155


University of Michigan

24. Dou, Xianzheng. The Computation-Data Duality in Non-Deterministic Systems.

Degree: PhD, Computer Science & Engineering, 2019, University of Michigan

 Repeated computations and redundant data are pervasively invoked or generated in modern systems and software. These computations and data are mostly, but not exactly the… (more)

Subjects/Keywords: data redundancy; computation redundancy; computation acceleration; equivalency between computation and data; nondeterminism; Computer Science; Engineering

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APA (6th Edition):

Dou, X. (2019). The Computation-Data Duality in Non-Deterministic Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151584

Chicago Manual of Style (16th Edition):

Dou, Xianzheng. “The Computation-Data Duality in Non-Deterministic Systems.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151584.

MLA Handbook (7th Edition):

Dou, Xianzheng. “The Computation-Data Duality in Non-Deterministic Systems.” 2019. Web. 19 Oct 2019.

Vancouver:

Dou X. The Computation-Data Duality in Non-Deterministic Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151584.

Council of Science Editors:

Dou X. The Computation-Data Duality in Non-Deterministic Systems. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151584

25. White, Mark. Generalizability of Scores from Classroom Observation Instruments.

Degree: PhD, Education, 2017, University of Michigan

 This dissertation examined the effect of contextual features of the classroom environment on measures of teacher quality derived from classroom observation instruments. Using data on… (more)

Subjects/Keywords: Generalizability Theory; Classroom Observation; Teacher Evaluation; Hidden Facet; Understanding Teacher Quality (UTQ); Situated Measurement; Education; Social Sciences (General); Statistics and Numeric Data; Social Sciences

…instruments. Using data on 228 teachers observed four times as part of the Understanding Teacher… …because occasions of observation were selected at random during the UTQ data collection period… …observation data measuring teaching quality (gathered over a particular set of situated… …a school, but the principal may only have data from a single class (or a few classes… …teachers are the best teachers across all schools in the district while having data on each 1… 

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APA (6th Edition):

White, M. (2017). Generalizability of Scores from Classroom Observation Instruments. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138742

Chicago Manual of Style (16th Edition):

White, Mark. “Generalizability of Scores from Classroom Observation Instruments.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/138742.

MLA Handbook (7th Edition):

White, Mark. “Generalizability of Scores from Classroom Observation Instruments.” 2017. Web. 19 Oct 2019.

Vancouver:

White M. Generalizability of Scores from Classroom Observation Instruments. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/138742.

Council of Science Editors:

White M. Generalizability of Scores from Classroom Observation Instruments. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138742


University of Michigan

26. Garikipati, Krishna Chaitanya. Towards Scalable Design of Future Wireless Networks.

Degree: PhD, Electrical Engineering: Systems, 2016, University of Michigan

 Wireless operators face an ever-growing challenge to meet the throughput and processing requirements of billions of devices that are getting connected. In current wireless networks,… (more)

Subjects/Keywords: wireless networks; cellular networks; baseband processing; Computer Science; Electrical Engineering; Engineering

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APA (6th Edition):

Garikipati, K. C. (2016). Towards Scalable Design of Future Wireless Networks. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133358

Chicago Manual of Style (16th Edition):

Garikipati, Krishna Chaitanya. “Towards Scalable Design of Future Wireless Networks.” 2016. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/133358.

MLA Handbook (7th Edition):

Garikipati, Krishna Chaitanya. “Towards Scalable Design of Future Wireless Networks.” 2016. Web. 19 Oct 2019.

Vancouver:

Garikipati KC. Towards Scalable Design of Future Wireless Networks. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/133358.

Council of Science Editors:

Garikipati KC. Towards Scalable Design of Future Wireless Networks. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133358


University of Michigan

27. Biwer, Craig. Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss.

Degree: PhD, Bioinformatics, 2017, University of Michigan

 Overweight and obesity are highly prevalent in the United States, with over two-thirds of the adult population classified as overweight and over one-third as obese.… (more)

Subjects/Keywords: Obesity; Signal processing; Machine learning; Health Sciences; Science

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APA (6th Edition):

Biwer, C. (2017). Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140907

Chicago Manual of Style (16th Edition):

Biwer, Craig. “Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/140907.

MLA Handbook (7th Edition):

Biwer, Craig. “Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss.” 2017. Web. 19 Oct 2019.

Vancouver:

Biwer C. Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/140907.

Council of Science Editors:

Biwer C. Computing Obesity: Signal Processing and Machine Learning Applied to Predictive Modeling of Clinical Weight-Loss. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140907


University of Michigan

28. Li, Fei. Querying RDBMS using Natural Language.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 It is often challenging to specify queries against a relational database since SQL requires its users to know the exact schema of the database, the… (more)

Subjects/Keywords: NLIDB; Natural Language Processing; Database Usability; Semantic Parsing; Computer Science; Engineering

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APA (6th Edition):

Li, F. (2017). Querying RDBMS using Natural Language. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138709

Chicago Manual of Style (16th Edition):

Li, Fei. “Querying RDBMS using Natural Language.” 2017. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/138709.

MLA Handbook (7th Edition):

Li, Fei. “Querying RDBMS using Natural Language.” 2017. Web. 19 Oct 2019.

Vancouver:

Li F. Querying RDBMS using Natural Language. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/138709.

Council of Science Editors:

Li F. Querying RDBMS using Natural Language. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138709


University of Michigan

29. Li, Hongyang. Computational Analysis of Physiological Systems at Multiple Time and Length Scales.

Degree: PhD, Bioinformatics, 2019, University of Michigan

 Advancements in unsupervised and supervised machine learning algorithms have provided new insights into data patterns and generated prediction models for practical usage. These machine learning… (more)

Subjects/Keywords: machine learning; deep learning; signal processing; proteogenomics; Science (General); Science

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APA (6th Edition):

Li, H. (2019). Computational Analysis of Physiological Systems at Multiple Time and Length Scales. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151445

Chicago Manual of Style (16th Edition):

Li, Hongyang. “Computational Analysis of Physiological Systems at Multiple Time and Length Scales.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/151445.

MLA Handbook (7th Edition):

Li, Hongyang. “Computational Analysis of Physiological Systems at Multiple Time and Length Scales.” 2019. Web. 19 Oct 2019.

Vancouver:

Li H. Computational Analysis of Physiological Systems at Multiple Time and Length Scales. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/151445.

Council of Science Editors:

Li H. Computational Analysis of Physiological Systems at Multiple Time and Length Scales. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151445


University of Michigan

30. Ding, Wei. Copula Regression Models for the Analysis of Correlated Data with Missing Values.

Degree: PhD, Biostatistics, 2015, University of Michigan

 The class of Gaussian copula regression models provides a unified modeling framework to accommodate various marginal distributions and flexible dependence structures. In the presence of… (more)

Subjects/Keywords: (Misaligned) Missing Data; Gaussian copula Regression Model; Composite Likelihood; Partial Identification; (Multilevel) Correlated Data; Peeling Optimzation Procedure; Statistics and Numeric Data; Science

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APA (6th Edition):

Ding, W. (2015). Copula Regression Models for the Analysis of Correlated Data with Missing Values. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113386

Chicago Manual of Style (16th Edition):

Ding, Wei. “Copula Regression Models for the Analysis of Correlated Data with Missing Values.” 2015. Doctoral Dissertation, University of Michigan. Accessed October 19, 2019. http://hdl.handle.net/2027.42/113386.

MLA Handbook (7th Edition):

Ding, Wei. “Copula Regression Models for the Analysis of Correlated Data with Missing Values.” 2015. Web. 19 Oct 2019.

Vancouver:

Ding W. Copula Regression Models for the Analysis of Correlated Data with Missing Values. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2027.42/113386.

Council of Science Editors:

Ding W. Copula Regression Models for the Analysis of Correlated Data with Missing Values. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113386

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