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You searched for +publisher:"Colorado State University" +contributor:("Cooley, Daniel S."). Showing records 1 – 8 of 8 total matches.

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Colorado State University

1. Russell, Brook T. Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling.

Degree: PhD, Statistics, 2015, Colorado State University

 This dissertation presents novel work in statistical methods for extremes. Our underlying modeling procedure identifies the linear combination of covariates that is associated with extreme… (more)

Subjects/Keywords: multivariate regular variation; cross validation; tail dependence

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APA (6th Edition):

Russell, B. T. (2015). Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167102

Chicago Manual of Style (16th Edition):

Russell, Brook T. “Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling.” 2015. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/167102.

MLA Handbook (7th Edition):

Russell, Brook T. “Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling.” 2015. Web. 14 Dec 2019.

Vancouver:

Russell BT. Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/167102.

Council of Science Editors:

Russell BT. Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167102


Colorado State University

2. Deshon, Jordan P. Stochastic analysis and probabilistic downscaling of soil moisture.

Degree: MS(M.S.), Civil and Environmental Engineering, 2018, Colorado State University

 Many applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil-moisture based on its… (more)

Subjects/Keywords: geostatistical analysis; soil moisture; variability; semivariograms; downscaling; spatial statistics

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APA (6th Edition):

Deshon, J. P. (2018). Stochastic analysis and probabilistic downscaling of soil moisture. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/191345

Chicago Manual of Style (16th Edition):

Deshon, Jordan P. “Stochastic analysis and probabilistic downscaling of soil moisture.” 2018. Masters Thesis, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/191345.

MLA Handbook (7th Edition):

Deshon, Jordan P. “Stochastic analysis and probabilistic downscaling of soil moisture.” 2018. Web. 14 Dec 2019.

Vancouver:

Deshon JP. Stochastic analysis and probabilistic downscaling of soil moisture. [Internet] [Masters thesis]. Colorado State University; 2018. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/191345.

Council of Science Editors:

Deshon JP. Stochastic analysis and probabilistic downscaling of soil moisture. [Masters Thesis]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191345


Colorado State University

3. Herman, Gregory Reid. New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A.

Degree: PhD, Atmospheric Science, 2019, Colorado State University

 High-impact weather comes in many different shapes, sizes, environments, and storm types, but all pose threats to human life, property, and the economy. Because of… (more)

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APA (6th Edition):

Herman, G. R. (2019). New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/193203

Chicago Manual of Style (16th Edition):

Herman, Gregory Reid. “New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A.” 2019. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/193203.

MLA Handbook (7th Edition):

Herman, Gregory Reid. “New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A.” 2019. Web. 14 Dec 2019.

Vancouver:

Herman GR. New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/193203.

Council of Science Editors:

Herman GR. New post-processing paradigm? : improving high-impact weather forecasts with machine learning, A. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/193203

4. Martin, Chad Allen. Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction.

Degree: MS(M.S.), Civil and Environmental Engineering, 2015, Colorado State University

 Seepage losses from unlined irrigation canals account for a large fraction of the total volume of water diverted for agricultural use, and reduction of these… (more)

Subjects/Keywords: Irrigation; Polyacrylamide; Uncertainty; Monte Carlo; Inflow-Outflow; Seepage

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APA (6th Edition):

Martin, C. A. (2015). Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/166923

Chicago Manual of Style (16th Edition):

Martin, Chad Allen. “Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction.” 2015. Masters Thesis, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/166923.

MLA Handbook (7th Edition):

Martin, Chad Allen. “Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction.” 2015. Web. 14 Dec 2019.

Vancouver:

Martin CA. Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction. [Internet] [Masters thesis]. Colorado State University; 2015. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/166923.

Council of Science Editors:

Martin CA. Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction. [Masters Thesis]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/166923

5. Scharf, Henry R. Statistical models for dependent trajectories with application to animal movement.

Degree: PhD, Statistics, 2018, Colorado State University

 In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical… (more)

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APA (6th Edition):

Scharf, H. R. (2018). Statistical models for dependent trajectories with application to animal movement. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/185778

Chicago Manual of Style (16th Edition):

Scharf, Henry R. “Statistical models for dependent trajectories with application to animal movement.” 2018. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/185778.

MLA Handbook (7th Edition):

Scharf, Henry R. “Statistical models for dependent trajectories with application to animal movement.” 2018. Web. 14 Dec 2019.

Vancouver:

Scharf HR. Statistical models for dependent trajectories with application to animal movement. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/185778.

Council of Science Editors:

Scharf HR. Statistical models for dependent trajectories with application to animal movement. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/185778

6. Sun, Wuliang. Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models.

Degree: PhD, Computer Science, 2015, Colorado State University

 Slicing is a reduction technique that has been applied to class models to support model comprehension, analysis, and other modeling activities. In particular, slicing techniques… (more)

Subjects/Keywords: Slicing; Class Model; UML

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APA (6th Edition):

Sun, W. (2015). Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/166933

Chicago Manual of Style (16th Edition):

Sun, Wuliang. “Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models.” 2015. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/166933.

MLA Handbook (7th Edition):

Sun, Wuliang. “Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models.” 2015. Web. 14 Dec 2019.

Vancouver:

Sun W. Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/166933.

Council of Science Editors:

Sun W. Using Slicing Techniques to Support Scalable Rigorous Analysis of Class Models. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/166933

7. Hewitt, Joshua. Statistical modeling and computing for climate data.

Degree: PhD, Statistics, 2019, Colorado State University

 The motivation for this thesis is to provide improved statistical models and approaches to statistical computing for analyzing climate patterns over short and long distances.… (more)

Subjects/Keywords: Climate; Statistical computing; Hierarchical modeling; Bayesian statistics

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APA (6th Edition):

Hewitt, J. (2019). Statistical modeling and computing for climate data. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/195408

Chicago Manual of Style (16th Edition):

Hewitt, Joshua. “Statistical modeling and computing for climate data.” 2019. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/195408.

MLA Handbook (7th Edition):

Hewitt, Joshua. “Statistical modeling and computing for climate data.” 2019. Web. 14 Dec 2019.

Vancouver:

Hewitt J. Statistical modeling and computing for climate data. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/195408.

Council of Science Editors:

Hewitt J. Statistical modeling and computing for climate data. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/195408


Colorado State University

8. Kolouri, Soheil. Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter.

Degree: PhD, Electrical and Computer Engineering, 2007, Colorado State University

 Tomography approaches are of great interests because of their non-intrusive nature and their ability to generate a significantly larger amount of data in comparison to… (more)

Subjects/Keywords: acoustic tomography; Unscented Kalman Filter; Fractal Brownian motion

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APA (6th Edition):

Kolouri, S. (2007). Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/71569

Chicago Manual of Style (16th Edition):

Kolouri, Soheil. “Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter.” 2007. Doctoral Dissertation, Colorado State University. Accessed December 14, 2019. http://hdl.handle.net/10217/71569.

MLA Handbook (7th Edition):

Kolouri, Soheil. “Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter.” 2007. Web. 14 Dec 2019.

Vancouver:

Kolouri S. Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter. [Internet] [Doctoral dissertation]. Colorado State University; 2007. [cited 2019 Dec 14]. Available from: http://hdl.handle.net/10217/71569.

Council of Science Editors:

Kolouri S. Acoustic tomography of the atmosphere using iterated Unscented Kalman Filter. [Doctoral Dissertation]. Colorado State University; 2007. Available from: http://hdl.handle.net/10217/71569

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