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You searched for +publisher:"South Dakota State University" +contributor:("Semhar Michael"). Showing records 1 – 5 of 5 total matches.

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1. Bae, Eric. Development of a Data-driven Patient Engagement Score Using Finite Mixture Models.

Degree: MS, Mathematics and Statistics, 2019, South Dakota State University

  Patient activation measure (PAM) is widely adopted by health care providers to access individual's knowledge, skill, and confidence for managing one's health and healthcare.… (more)

Subjects/Keywords: Finite mixture models; Multiple linear regression; Patient activation measure; Patient engagement; Mathematics; Statistics and Probability

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APA (6th Edition):

Bae, E. (2019). Development of a Data-driven Patient Engagement Score Using Finite Mixture Models. (Masters Thesis). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/3391

Chicago Manual of Style (16th Edition):

Bae, Eric. “Development of a Data-driven Patient Engagement Score Using Finite Mixture Models.” 2019. Masters Thesis, South Dakota State University. Accessed August 08, 2020. https://openprairie.sdstate.edu/etd/3391.

MLA Handbook (7th Edition):

Bae, Eric. “Development of a Data-driven Patient Engagement Score Using Finite Mixture Models.” 2019. Web. 08 Aug 2020.

Vancouver:

Bae E. Development of a Data-driven Patient Engagement Score Using Finite Mixture Models. [Internet] [Masters thesis]. South Dakota State University; 2019. [cited 2020 Aug 08]. Available from: https://openprairie.sdstate.edu/etd/3391.

Council of Science Editors:

Bae E. Development of a Data-driven Patient Engagement Score Using Finite Mixture Models. [Masters Thesis]. South Dakota State University; 2019. Available from: https://openprairie.sdstate.edu/etd/3391

2. Bayer, Damon. Variable Selection Techniques for Clustering on the Unit Hypersphere.

Degree: MS, Mathematics and Statistics, 2018, South Dakota State University

  Mixtures of von Mises-Fisher distributions have been shown to be an effective model for clustering data on a unit hypersphere, but variable selection for… (more)

Subjects/Keywords: Custering; directional distributions; expectation maximization; mixtures; variable selection; von Mises-Fisher; Statistics and Probability

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APA (6th Edition):

Bayer, D. (2018). Variable Selection Techniques for Clustering on the Unit Hypersphere. (Masters Thesis). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/2652

Chicago Manual of Style (16th Edition):

Bayer, Damon. “Variable Selection Techniques for Clustering on the Unit Hypersphere.” 2018. Masters Thesis, South Dakota State University. Accessed August 08, 2020. https://openprairie.sdstate.edu/etd/2652.

MLA Handbook (7th Edition):

Bayer, Damon. “Variable Selection Techniques for Clustering on the Unit Hypersphere.” 2018. Web. 08 Aug 2020.

Vancouver:

Bayer D. Variable Selection Techniques for Clustering on the Unit Hypersphere. [Internet] [Masters thesis]. South Dakota State University; 2018. [cited 2020 Aug 08]. Available from: https://openprairie.sdstate.edu/etd/2652.

Council of Science Editors:

Bayer D. Variable Selection Techniques for Clustering on the Unit Hypersphere. [Masters Thesis]. South Dakota State University; 2018. Available from: https://openprairie.sdstate.edu/etd/2652

3. Abdalla, Abdelbaset. Finite Mixture of Regression Models for Complex Survey Data.

Degree: PhD, Mathematics and Statistics, 2019, South Dakota State University

  Over time, survey data has become an essential source of information for modern society. However, to be effective, the structures of survey data require… (more)

Subjects/Keywords: BIC; Complex Survey Design; Finite Mixture of Regression Models; pseudo-likelihood; Sampling Weights; The expectation-maximization algorithm; Statistics and Probability

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APA (6th Edition):

Abdalla, A. (2019). Finite Mixture of Regression Models for Complex Survey Data. (Doctoral Dissertation). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/3629

Chicago Manual of Style (16th Edition):

Abdalla, Abdelbaset. “Finite Mixture of Regression Models for Complex Survey Data.” 2019. Doctoral Dissertation, South Dakota State University. Accessed August 08, 2020. https://openprairie.sdstate.edu/etd/3629.

MLA Handbook (7th Edition):

Abdalla, Abdelbaset. “Finite Mixture of Regression Models for Complex Survey Data.” 2019. Web. 08 Aug 2020.

Vancouver:

Abdalla A. Finite Mixture of Regression Models for Complex Survey Data. [Internet] [Doctoral dissertation]. South Dakota State University; 2019. [cited 2020 Aug 08]. Available from: https://openprairie.sdstate.edu/etd/3629.

Council of Science Editors:

Abdalla A. Finite Mixture of Regression Models for Complex Survey Data. [Doctoral Dissertation]. South Dakota State University; 2019. Available from: https://openprairie.sdstate.edu/etd/3629

4. Khan, Md Riaz Ahmed. Data Center Load Forecast Using Dependent Mixture Model.

Degree: MS, Electrical Engineering and Computer Science, 2016, South Dakota State University

  The dependency on cloud computing is increasing day by day. With the boom of data centers, the cost is also increasing, which forces industries… (more)

Subjects/Keywords: data center; load forcast; mixture model; Computer Engineering; Data Storage Systems; Electrical and Computer Engineering

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APA (6th Edition):

Khan, M. R. A. (2016). Data Center Load Forecast Using Dependent Mixture Model. (Masters Thesis). South Dakota State University. Retrieved from http://openprairie.sdstate.edu/etd/1120

Chicago Manual of Style (16th Edition):

Khan, Md Riaz Ahmed. “Data Center Load Forecast Using Dependent Mixture Model.” 2016. Masters Thesis, South Dakota State University. Accessed August 08, 2020. http://openprairie.sdstate.edu/etd/1120.

MLA Handbook (7th Edition):

Khan, Md Riaz Ahmed. “Data Center Load Forecast Using Dependent Mixture Model.” 2016. Web. 08 Aug 2020.

Vancouver:

Khan MRA. Data Center Load Forecast Using Dependent Mixture Model. [Internet] [Masters thesis]. South Dakota State University; 2016. [cited 2020 Aug 08]. Available from: http://openprairie.sdstate.edu/etd/1120.

Council of Science Editors:

Khan MRA. Data Center Load Forecast Using Dependent Mixture Model. [Masters Thesis]. South Dakota State University; 2016. Available from: http://openprairie.sdstate.edu/etd/1120

5. Shakya, Ayush. Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System.

Degree: MS, Electrical Engineering and Computer Science, 2016, South Dakota State University

  Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power… (more)

Subjects/Keywords: Clear Sky Irradiance Energy Management System Fourier Basis Expansion OPAL-RT; Real-time Digital Simulator; solar irradiance forecasting; Electrical and Computer Engineering; Power and Energy

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APA (6th Edition):

Shakya, A. (2016). Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System. (Masters Thesis). South Dakota State University. Retrieved from http://openprairie.sdstate.edu/etd/1068

Chicago Manual of Style (16th Edition):

Shakya, Ayush. “Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System.” 2016. Masters Thesis, South Dakota State University. Accessed August 08, 2020. http://openprairie.sdstate.edu/etd/1068.

MLA Handbook (7th Edition):

Shakya, Ayush. “Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System.” 2016. Web. 08 Aug 2020.

Vancouver:

Shakya A. Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System. [Internet] [Masters thesis]. South Dakota State University; 2016. [cited 2020 Aug 08]. Available from: http://openprairie.sdstate.edu/etd/1068.

Council of Science Editors:

Shakya A. Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System. [Masters Thesis]. South Dakota State University; 2016. Available from: http://openprairie.sdstate.edu/etd/1068

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