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You searched for +publisher:"Texas A&M University – Corpus Christi" +contributor:("Guardiola, Jose"). Showing records 1 – 2 of 2 total matches.

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1. Geller, Adam. Calculating Kendall's Tau with multiple measurements .

Degree: 2018, Texas A&M University – Corpus Christi

Relationships between time series of environmental variables are commonly calculated using non-parametric methods, such as Kendall’s τ, because of ”non-detects”, i.e., left-censored data that falls below a measurement limit. However, these methods are not well-adapted to situations where variables have multiple contemporaneous measurements. In this thesis, we define a new method, τ ̃, in an attempt to calculate correlations using each of the multiple measurements instead of daily means. We investigate τ ̃ using two methods: simulations that approximate a null distribution for τ ̃ and closed form calculations for a specific special case. We also apply τ ̃ to an actual data set. The results of our investigation shows that τ ̃ may handle certain things, such as outliers, better than current methods. However, its requirements for distributional assumptions about the data make it a less practical option for real data. Further work could explore ways to avoid the prerequisite need for distribution knowledge and could also further investigate τ ̃ under noise sampled from asymmetric distributions. Advisors/Committee Members: Sterba-Boatwright, Blair (advisor), Jin, Lei (committeeMember), Guardiola, Jose (committeeMember).

Subjects/Keywords: Correlation; Kendall; Rank; Tau

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

APA (6th Edition):

Geller, A. (2018). Calculating Kendall's Tau with multiple measurements . (Thesis). Texas A&M University – Corpus Christi. Retrieved from http://hdl.handle.net/1969.6/31357

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Geller, Adam. “Calculating Kendall's Tau with multiple measurements .” 2018. Thesis, Texas A&M University – Corpus Christi. Accessed July 19, 2019. http://hdl.handle.net/1969.6/31357.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Geller, Adam. “Calculating Kendall's Tau with multiple measurements .” 2018. Web. 19 Jul 2019.

Vancouver:

Geller A. Calculating Kendall's Tau with multiple measurements . [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2018. [cited 2019 Jul 19]. Available from: http://hdl.handle.net/1969.6/31357.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Geller A. Calculating Kendall's Tau with multiple measurements . [Thesis]. Texas A&M University – Corpus Christi; 2018. Available from: http://hdl.handle.net/1969.6/31357

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Lai, Chinh Thi Tuyet. Respiratory disease diagnosis for dolphin using breath data .

Degree: 2017, Texas A&M University – Corpus Christi

Respiratory disease in marine mammals evokes strong public attention as well as worthwhile scientific interest. Traditional methods for animal disease diagnosis include blood test, ultrasound, and computed tomography scan. These methods require invasive equipment to perform, and cannot be applied to free-swimming animals. Breath data, the measurement of lung inflow and outflow while breathing, can be collected from free-swimming animals in a non-invasive way, and so is less stressful for distressed animals. However, because of new features in the data, new statistical methods are required for the breath data analysis. In this thesis, we investigate one potential method for analyzing breath data. Our method begins by decomposing a raw dataset containing a sequence of breath cycles into a set of individual breath cycles. Incomplete cycles are removed from the dataset. In this research, we consider an entire breath cycle to be one unit of observation. Starting and ending points of breath cycles can be difficult to determine, and cause a large amount of variation in size and shape of breath curves. To reduce cycle to cycle variability, we apply curve registration to synchronize a set of breath cycles. Breath cycles are described using magnitude information and geometric shape information. We propose three shape models, namely, simple oval model, quadratic spline model, and piecewise linear model. Furthermore, principal component analysis is applied to the magnitude/shape descriptors to obtain main features of breath cycles. Criteria for disease diagnosis are developed by identifying key differences among these main features between healthy and unhealthy animals. The proposed methods were applied to check if two testing animals are diseased or not. The results were consistent with the status of both animals. Advisors/Committee Members: Jin, Lei (advisor), Guardiola, Jose (committeeMember), Sterba-Boatwright, Blair (committeeMember).

Subjects/Keywords: breath data; dolphin; functional data analysis; geometric models; respiratory disease diagnosis; principal component analysis

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

APA (6th Edition):

Lai, C. T. T. (2017). Respiratory disease diagnosis for dolphin using breath data . (Thesis). Texas A&M University – Corpus Christi. Retrieved from http://hdl.handle.net/1969.6/19202

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lai, Chinh Thi Tuyet. “Respiratory disease diagnosis for dolphin using breath data .” 2017. Thesis, Texas A&M University – Corpus Christi. Accessed July 19, 2019. http://hdl.handle.net/1969.6/19202.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lai, Chinh Thi Tuyet. “Respiratory disease diagnosis for dolphin using breath data .” 2017. Web. 19 Jul 2019.

Vancouver:

Lai CTT. Respiratory disease diagnosis for dolphin using breath data . [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2017. [cited 2019 Jul 19]. Available from: http://hdl.handle.net/1969.6/19202.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lai CTT. Respiratory disease diagnosis for dolphin using breath data . [Thesis]. Texas A&M University – Corpus Christi; 2017. Available from: http://hdl.handle.net/1969.6/19202

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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