Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"Virginia Tech" +contributor:("Ferreira, Marco Antonio Rosa"). Showing records 1 – 6 of 6 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Virginia Tech

1. Porter, Erica May. Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data.

Degree: MS, Statistics, 2019, Virginia Tech

 Spatial data is increasingly relevant in a wide variety of research areas. Economists, medical researchers, ecologists, and policymakers all make critical decisions about populations using… (more)

Subjects/Keywords: Bayesian Analysis; Spatial Statistics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Porter, E. M. (2019). Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91385

Chicago Manual of Style (16th Edition):

Porter, Erica May. “Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data.” 2019. Masters Thesis, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/91385.

MLA Handbook (7th Edition):

Porter, Erica May. “Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data.” 2019. Web. 10 Aug 2020.

Vancouver:

Porter EM. Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/91385.

Council of Science Editors:

Porter EM. Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91385


Virginia Tech

2. Carzolio, Marcos Arantes. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.

Degree: PhD, Statistics, 2016, Virginia Tech

 We are entering an exciting era, rich in the availability of data via sources such as the Internet, satellites, particle colliders, telecommunication networks, computer simulations,… (more)

Subjects/Keywords: Markov chain Monte Carlo; reversible jump; weighted particle tempering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Carzolio, M. A. (2016). On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71765

Chicago Manual of Style (16th Edition):

Carzolio, Marcos Arantes. “On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.” 2016. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/71765.

MLA Handbook (7th Edition):

Carzolio, Marcos Arantes. “On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.” 2016. Web. 10 Aug 2020.

Vancouver:

Carzolio MA. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/71765.

Council of Science Editors:

Carzolio MA. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71765


Virginia Tech

3. Hoegh, Andrew B. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.

Degree: PhD, Statistics, 2016, Virginia Tech

 Data sets of increasing size and complexity require new approaches for prediction as the sheer volume of data from disparate sources inhibits joint processing and… (more)

Subjects/Keywords: Model Fusion; Spatiotemporal Modeling; Areal Data; Sequential Monte Carlo

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hoegh, A. B. (2016). Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/70921

Chicago Manual of Style (16th Edition):

Hoegh, Andrew B. “Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.” 2016. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/70921.

MLA Handbook (7th Edition):

Hoegh, Andrew B. “Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.” 2016. Web. 10 Aug 2020.

Vancouver:

Hoegh AB. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/70921.

Council of Science Editors:

Hoegh AB. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/70921


Virginia Tech

4. Metzger, Thomas Anthony. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.

Degree: PhD, Statistics, 2019, Virginia Tech

 Statistical models are a powerful tool for describing a broad range of phenomena in our world. However, many common statistical models may make assumptions that… (more)

Subjects/Keywords: model selection; heteroscedasticity; linear models; Bayesian

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Metzger, T. A. (2019). Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93226

Chicago Manual of Style (16th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/93226.

MLA Handbook (7th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Web. 10 Aug 2020.

Vancouver:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/93226.

Council of Science Editors:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93226

5. Keefe, Matthew James. Statistical Monitoring and Modeling for Spatial Processes.

Degree: PhD, Statistics, 2017, Virginia Tech

 Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components:… (more)

Subjects/Keywords: Bayesian Analysis; Objective Priors; Risk-adjustment; Spatial Statistics; Statistical Process Monitoring

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Keefe, M. J. (2017). Statistical Monitoring and Modeling for Spatial Processes. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/76664

Chicago Manual of Style (16th Edition):

Keefe, Matthew James. “Statistical Monitoring and Modeling for Spatial Processes.” 2017. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/76664.

MLA Handbook (7th Edition):

Keefe, Matthew James. “Statistical Monitoring and Modeling for Spatial Processes.” 2017. Web. 10 Aug 2020.

Vancouver:

Keefe MJ. Statistical Monitoring and Modeling for Spatial Processes. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/76664.

Council of Science Editors:

Keefe MJ. Statistical Monitoring and Modeling for Spatial Processes. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/76664


Virginia Tech

6. Sun, Furong. Some Advances in Local Approximate Gaussian Processes.

Degree: PhD, Statistics, 2019, Virginia Tech

 Nowadays, Gaussian Process (GP) has been recognized as an indispensable statistical tool in computer experiments. Due to its computational complexity and storage demand, its application… (more)

Subjects/Keywords: nonparametric regression; approximate kriging; multilevel modeling; calibration; space-filling design; inverse-variance weighting

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sun, F. (2019). Some Advances in Local Approximate Gaussian Processes. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/97245

Chicago Manual of Style (16th Edition):

Sun, Furong. “Some Advances in Local Approximate Gaussian Processes.” 2019. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/97245.

MLA Handbook (7th Edition):

Sun, Furong. “Some Advances in Local Approximate Gaussian Processes.” 2019. Web. 10 Aug 2020.

Vancouver:

Sun F. Some Advances in Local Approximate Gaussian Processes. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/97245.

Council of Science Editors:

Sun F. Some Advances in Local Approximate Gaussian Processes. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/97245

.