With the increase in computation and data storage, there has been a vast collection of information gained with scientific measurement
devices. However, with this increase in data and variety of domain applications, statistical methodology must be tailored to specific problems.
This dissertation is focused on analyzing chemical information with an underlying structure. Robust fused lasso leverages information about the
neighboring regression coefficient structure to create blocks of coefficients. Robust modifications are made to the mean to account for gross
outliers in the data. This method is applied to near infrared spectral measurements in prediction of an aqueous analyte concentration and is
shown to improve prediction accuracy. Expansion on the robust estimation and structure analysis is performed by examining graph structures
within a clustered tensor. The tensor is subjected to wavelet smoothing and robust sparse precision matrix estimation for a detailed look into
the covariance structure. This methodology is applied to catalytic kinetics data where the graph structure estimates the elementary steps within
the reaction mechanism. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the
degree of Doctor of Philosophy. / Fall Semester 2018. / October 18, 2018. / Includes bibliographical references. / Yiyuan She, Professor Directing Dissertation; Albert Stiegman, University Representative; Qing Mai,
Committee Member; Eric Chicken, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_661158 |
| Contributors | Kunz, Matthew Ross (author), She, Yiyuan (professor directing dissertation), Stiegman, Albert E., 1953- (university representative), Mai, Qing (committee member), Chicken, Eric, 1963- (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (82 pages), computer, application/pdf |
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