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You searched for +publisher:"University of Michigan" +contributor:("Berrocal, Veronica J"). Showing records 1 – 7 of 7 total matches.

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University of Michigan

1. Bhattacharya, Shrijita. Outlier Identification in Spatio-Temporal Processes.

Degree: PhD, Statistics, 2018, University of Michigan

 This dissertation answers some of the statistical challenges arising in spatio-temporal data from Internet traffic, electricity grids and climate models. It begins with methodological contributions… (more)

Subjects/Keywords: Outlier identification in Spatio-Temporal Models; Statistics and Numeric Data; Science

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APA (6th Edition):

Bhattacharya, S. (2018). Outlier Identification in Spatio-Temporal Processes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145789

Chicago Manual of Style (16th Edition):

Bhattacharya, Shrijita. “Outlier Identification in Spatio-Temporal Processes.” 2018. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/145789.

MLA Handbook (7th Edition):

Bhattacharya, Shrijita. “Outlier Identification in Spatio-Temporal Processes.” 2018. Web. 14 Nov 2019.

Vancouver:

Bhattacharya S. Outlier Identification in Spatio-Temporal Processes. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/145789.

Council of Science Editors:

Bhattacharya S. Outlier Identification in Spatio-Temporal Processes. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145789


University of Michigan

2. Sun, Wenbo. Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications.

Degree: PhD, Industrial & Operations Engineering, 2018, University of Michigan

 Massive data are feasibly collected or generated with the rapid development of sensing, high computing and computer simulation technologies. Among various types of data, functional… (more)

Subjects/Keywords: Functional Data Analysis; Uncertainty Quantification; Statistical Analysis; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Sun, W. (2018). Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149782

Chicago Manual of Style (16th Edition):

Sun, Wenbo. “Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications.” 2018. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/149782.

MLA Handbook (7th Edition):

Sun, Wenbo. “Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications.” 2018. Web. 14 Nov 2019.

Vancouver:

Sun W. Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/149782.

Council of Science Editors:

Sun W. Uncertainty Quantification Methodologies for Functional Data in Biomechanical Applications. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/149782


University of Michigan

3. Arain, Aubrey. Environmental Health Impacts of Informal Electronic Waste Recycling.

Degree: PhD, Environmental Health Sciences, 2019, University of Michigan

 Electronic waste, “E-waste”, is the fastest growing waste stream globally. Informal e-waste recycling lacks the policy and regulatory controls found in formal industry, creating health… (more)

Subjects/Keywords: Metal Exposure; E-waste; Occupational Health; Injury; Material Flow Analysis; Life Cycle Assessment; Public Health; Health Sciences

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APA (6th Edition):

Arain, A. (2019). Environmental Health Impacts of Informal Electronic Waste Recycling. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149875

Chicago Manual of Style (16th Edition):

Arain, Aubrey. “Environmental Health Impacts of Informal Electronic Waste Recycling.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/149875.

MLA Handbook (7th Edition):

Arain, Aubrey. “Environmental Health Impacts of Informal Electronic Waste Recycling.” 2019. Web. 14 Nov 2019.

Vancouver:

Arain A. Environmental Health Impacts of Informal Electronic Waste Recycling. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/149875.

Council of Science Editors:

Arain A. Environmental Health Impacts of Informal Electronic Waste Recycling. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149875


University of Michigan

4. Tu, Chun-Chen. Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings.

Degree: PhD, Statistics, 2019, University of Michigan

 Regression is a widely used statistical tool to discover associations between variables. Estimated relationships can be further utilized for predicting new observations. Obtaining reliable prediction… (more)

Subjects/Keywords: Mixture of regressions; Robust regression; Adversarial machine learning; Statistics and Numeric Data; Science

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APA (6th Edition):

Tu, C. (2019). Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149938

Chicago Manual of Style (16th Edition):

Tu, Chun-Chen. “Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/149938.

MLA Handbook (7th Edition):

Tu, Chun-Chen. “Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings.” 2019. Web. 14 Nov 2019.

Vancouver:

Tu C. Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/149938.

Council of Science Editors:

Tu C. Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149938


University of Michigan

5. Shen, Jinqi. Local Structure of Random Fields - Properties and Inference.

Degree: PhD, Statistics, 2019, University of Michigan

 Advances in data collection and computation tools popularize localized modeling on temporal or spatial data. Similar to the connection between derivatives and smooth functions, one… (more)

Subjects/Keywords: Tangent Field; Spatial Statistics; Multifractional Brownian motion; Spectral measure; Functional data analysis; Hurst Index; Statistics and Numeric Data; Science

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APA (6th Edition):

Shen, J. (2019). Local Structure of Random Fields - Properties and Inference. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151592

Chicago Manual of Style (16th Edition):

Shen, Jinqi. “Local Structure of Random Fields - Properties and Inference.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/151592.

MLA Handbook (7th Edition):

Shen, Jinqi. “Local Structure of Random Fields - Properties and Inference.” 2019. Web. 14 Nov 2019.

Vancouver:

Shen J. Local Structure of Random Fields - Properties and Inference. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/151592.

Council of Science Editors:

Shen J. Local Structure of Random Fields - Properties and Inference. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151592


University of Michigan

6. 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 November 14, 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. 14 Nov 2019.

Vancouver:

Guo C. Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 14]. 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

7. Havumaki, Joshua. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.

Degree: PhD, Epidemiological Science, 2019, University of Michigan

 Analyzing epidemiological data (e.g., from observational studies or surveillance) can reveal results contrary to what might be expected given a priori knowledge about the study… (more)

Subjects/Keywords: transmission modeling; norovirus; obesity paradox; tuberculosis; directed acyclic graphs; compartmental models; Public Health; Mathematics; Science (General); Statistics and Numeric Data; Health Sciences; Science

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

APA (6th Edition):

Havumaki, J. (2019). Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151666

Chicago Manual of Style (16th Edition):

Havumaki, Joshua. “Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.” 2019. Doctoral Dissertation, University of Michigan. Accessed November 14, 2019. http://hdl.handle.net/2027.42/151666.

MLA Handbook (7th Edition):

Havumaki, Joshua. “Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data.” 2019. Web. 14 Nov 2019.

Vancouver:

Havumaki J. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Nov 14]. Available from: http://hdl.handle.net/2027.42/151666.

Council of Science Editors:

Havumaki J. Using Mathematical Models to Understand Causal Mechanisms Underlying Counterintuitive Epidemiological Data. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151666

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