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You searched for +publisher:"Virginia Tech" +contributor:("Franck, Christopher Thomas"). Showing records 1 – 14 of 14 total matches.

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Virginia Tech

1. Beringer, Danielle Nicole. An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait.

Degree: MS, Biomedical Engineering, 2013, Virginia Tech

 The biomechanics of slips are commonly studied in laboratory settings in an effort to improve the understanding of slip mechanisms for the advancement of slip… (more)

Subjects/Keywords: Biomechanics; Slips and falls; Gait adaptations; RCOF

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APA (6th Edition):

Beringer, D. N. (2013). An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23079

Chicago Manual of Style (16th Edition):

Beringer, Danielle Nicole. “An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait.” 2013. Masters Thesis, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/23079.

MLA Handbook (7th Edition):

Beringer, Danielle Nicole. “An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait.” 2013. Web. 10 Aug 2020.

Vancouver:

Beringer DN. An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/23079.

Council of Science Editors:

Beringer DN. An Exploratory Study Investigating the Time Duration of Slip-Induced Changes in Gait. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23079


Virginia Tech

2. Varghese, Robin. Novel Prognostic Markers and Therapeutic Targets for Glioblastoma.

Degree: PhD, Animal and Poultry Sciences, 2016, Virginia Tech

 Glioblastoma is the most common and lethal malignant brain tumor with a survival rate of 14.6 months and a tumor recurrence rate of ninety percent.… (more)

Subjects/Keywords: Glioblastoma; Tumor Recurrence; GBM; Prognostic Markers; CSNK1E; PIK3CB

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APA (6th Edition):

Varghese, R. (2016). Novel Prognostic Markers and Therapeutic Targets for Glioblastoma. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71420

Chicago Manual of Style (16th Edition):

Varghese, Robin. “Novel Prognostic Markers and Therapeutic Targets for Glioblastoma.” 2016. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/71420.

MLA Handbook (7th Edition):

Varghese, Robin. “Novel Prognostic Markers and Therapeutic Targets for Glioblastoma.” 2016. Web. 10 Aug 2020.

Vancouver:

Varghese R. Novel Prognostic Markers and Therapeutic Targets for Glioblastoma. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/71420.

Council of Science Editors:

Varghese R. Novel Prognostic Markers and Therapeutic Targets for Glioblastoma. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71420


Virginia Tech

3. 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

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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

4. Guan, Ting. Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals.

Degree: PhD, Statistics, 2018, Virginia Tech

 Genetic association studies usually include related individuals. Meanwhile, high-throughput sequencing technologies produce data of multiple genetic variants. Due to linkage disequilibrium (LD) and familial relatedness,… (more)

Subjects/Keywords: genetic association test; related Individuals; burden test; genotype imputation; longitudinal study

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APA (6th Edition):

Guan, T. (2018). Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/96243

Chicago Manual of Style (16th Edition):

Guan, Ting. “Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals.” 2018. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/96243.

MLA Handbook (7th Edition):

Guan, Ting. “Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals.” 2018. Web. 10 Aug 2020.

Vancouver:

Guan T. Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/96243.

Council of Science Editors:

Guan T. Novel Statistical Methods for Multiple-variant Genetic Association Studies with Related Individuals. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/96243


Virginia Tech

5. Shen, Sumin. Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency.

Degree: PhD, Statistics, 2020, Virginia Tech

 The advances in data-collecting technologies provides great opportunities to access large sample-size data sets with high dimensionality. Variable selection is an important procedure to extract… (more)

Subjects/Keywords: Model Selection; Nonnegative Garrote Method; Mixture Experiments; Dynamic Coefficient; Fused Lasso; Group Lasso; Alternative Direction Method of Multipliers (ADMM); Integer Optimization; Expectation-Maximization (EM); LASSO.

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APA (6th Edition):

Shen, S. (2020). Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/96767

Chicago Manual of Style (16th Edition):

Shen, Sumin. “Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency.” 2020. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/96767.

MLA Handbook (7th Edition):

Shen, Sumin. “Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency.” 2020. Web. 10 Aug 2020.

Vancouver:

Shen S. Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/96767.

Council of Science Editors:

Shen S. Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/96767


Virginia Tech

6. Choi, Hyunjin. An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation.

Degree: PhD, Animal and Poultry Sciences, 2013, Virginia Tech

 Pathogens colonize their hosts by releasing molecules that can enter host cells. A biotrophic oomycete plant pathogen, Phytophthora sojae harbors a superfamily of effector genes… (more)

Subjects/Keywords: Protein function prediction; Phytophthora sojae; effectors; support vector machines; functional linkage network; amino acid physical properties

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APA (6th Edition):

Choi, H. (2013). An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51762

Chicago Manual of Style (16th Edition):

Choi, Hyunjin. “An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation.” 2013. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/51762.

MLA Handbook (7th Edition):

Choi, Hyunjin. “An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation.” 2013. Web. 10 Aug 2020.

Vancouver:

Choi H. An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/51762.

Council of Science Editors:

Choi H. An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51762


Virginia Tech

7. Mellis, Alexandra Michelle. Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology.

Degree: PhD, Translational Biology, Medicine and Health, 2019, Virginia Tech

 Health behaviors, positive and negative, can support or reduce risk for multiple chronic diseases, such as substance use disorder and obesity. These diseases are marked… (more)

Subjects/Keywords: Addiction; health risk behaviors; obesity; reinforcer pathology

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APA (6th Edition):

Mellis, A. M. (2019). Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/88485

Chicago Manual of Style (16th Edition):

Mellis, Alexandra Michelle. “Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology.” 2019. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/88485.

MLA Handbook (7th Edition):

Mellis, Alexandra Michelle. “Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology.” 2019. Web. 10 Aug 2020.

Vancouver:

Mellis AM. Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/88485.

Council of Science Editors:

Mellis AM. Quantifying the Effects of a Constricted Temporal Window in Reinforcer Pathology. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/88485


Virginia Tech

8. 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

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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

9. 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

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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

10. Ortega Villa, Ana Maria. Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies.

Degree: PhD, Statistics, 2015, Virginia Tech

 Semiparametric modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown… (more)

Subjects/Keywords: Bayesian Nonparametric; Conditional logistic regression; Matched case-control study; Regression splines; Spatial-temporal data; Varying Coefficient Model

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APA (6th Edition):

Ortega Villa, A. M. (2015). Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64181

Chicago Manual of Style (16th Edition):

Ortega Villa, Ana Maria. “Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies.” 2015. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/64181.

MLA Handbook (7th Edition):

Ortega Villa, Ana Maria. “Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies.” 2015. Web. 10 Aug 2020.

Vancouver:

Ortega Villa AM. Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/64181.

Council of Science Editors:

Ortega Villa AM. Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64181

11. Vijayan, Vinaya. Understanding and Improving Identification of Somatic Variants.

Degree: PhD, Animal and Poultry Sciences, 2016, Virginia Tech

 It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of… (more)

Subjects/Keywords: Somatic variants; Somatic variant callers; Somatic point mutations; Short tandem repeat variation; Lung squamous cell carcinoma

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APA (6th Edition):

Vijayan, V. (2016). Understanding and Improving Identification of Somatic Variants. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/72969

Chicago Manual of Style (16th Edition):

Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/72969.

MLA Handbook (7th Edition):

Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Web. 10 Aug 2020.

Vancouver:

Vijayan V. Understanding and Improving Identification of Somatic Variants. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/72969.

Council of Science Editors:

Vijayan V. Understanding and Improving Identification of Somatic Variants. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/72969

12. Huang, Jiangeng. Sequential learning, large-scale calibration, and uncertainty quantification.

Degree: PhD, Statistics, 2019, Virginia Tech

 With remarkable advances in computing power, complex physical systems today can be simulated comparatively cheaply and to high accuracy through computer experiments. Computer experiments continue… (more)

Subjects/Keywords: sequential learning; computer experiments; uncertainty quantification; big data; hierarchical modeling

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APA (6th Edition):

Huang, J. (2019). Sequential learning, large-scale calibration, and uncertainty quantification. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91935

Chicago Manual of Style (16th Edition):

Huang, Jiangeng. “Sequential learning, large-scale calibration, and uncertainty quantification.” 2019. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/91935.

MLA Handbook (7th Edition):

Huang, Jiangeng. “Sequential learning, large-scale calibration, and uncertainty quantification.” 2019. Web. 10 Aug 2020.

Vancouver:

Huang J. Sequential learning, large-scale calibration, and uncertainty quantification. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/91935.

Council of Science Editors:

Huang J. Sequential learning, large-scale calibration, and uncertainty quantification. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91935

13. Garman, Christina Maria Rossi. Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery.

Degree: PhD, Engineering Science and Mechanics, 2015, Virginia Tech

 Fall related injuries are a major public health concern due to their high associated medical costs and negative impact on quality of life. Obese and… (more)

Subjects/Keywords: Obesity; Aging; Likelihood of tripping; Balance recovery

Virginia Tech. Matrangola, S. L., D. E. Anderson and M. L. Madigan (2011). Preliminary… 

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APA (6th Edition):

Garman, C. M. R. (2015). Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51690

Chicago Manual of Style (16th Edition):

Garman, Christina Maria Rossi. “Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery.” 2015. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/51690.

MLA Handbook (7th Edition):

Garman, Christina Maria Rossi. “Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery.” 2015. Web. 10 Aug 2020.

Vancouver:

Garman CMR. Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/51690.

Council of Science Editors:

Garman CMR. Understanding the effects of obesity and age on likelihood of tripping and subsequent balance recovery. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/51690


Virginia Tech

14. Highnam, Gareth Wei An. Optimizing analysis pipelines for improved variant discovery.

Degree: PhD, Animal and Poultry Sciences, 2014, Virginia Tech

Subjects/Keywords: Genomics; sequencing; genetics; optimization; bioinformatics

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APA (6th Edition):

Highnam, G. W. A. (2014). Optimizing analysis pipelines for improved variant discovery. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/47451

Chicago Manual of Style (16th Edition):

Highnam, Gareth Wei An. “Optimizing analysis pipelines for improved variant discovery.” 2014. Doctoral Dissertation, Virginia Tech. Accessed August 10, 2020. http://hdl.handle.net/10919/47451.

MLA Handbook (7th Edition):

Highnam, Gareth Wei An. “Optimizing analysis pipelines for improved variant discovery.” 2014. Web. 10 Aug 2020.

Vancouver:

Highnam GWA. Optimizing analysis pipelines for improved variant discovery. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Aug 10]. Available from: http://hdl.handle.net/10919/47451.

Council of Science Editors:

Highnam GWA. Optimizing analysis pipelines for improved variant discovery. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/47451

.