Masters Theses


“The most commonly used metric for evaluating alertness and vigilance is the Psychomotor Vigilance Test (PVT), previous studies have indicated that alertness and vigilance can be affected by the lack of sleep as a function of sleep loss. This study explores methods to predict median psychomotor vigilance reaction times. The data used in this study comes from a series of tests and surveys conducted on volunteer students. The data set contains many potential predictors of PVT and one aspect of the study was to identify variables that are useful in prediction. The performances of various prediction methods that allow for feature selection were evaluated. Prediction errors were estimated by using ten-fold validation method and root mean squared error was employed to compare the methods.

Results show that the linear model with LASSO feature selection provide the best predictions of psychomotor vigilance test median reaction time in this context. Moreover, we were able to identify subsets of predictors that lead to reduced prediction error and are useful for extracting biological insights. The linear mixed model and canonical correlation analysis provided information on what factors affect vigilance attention, and what cognitive functions are affected by sleep quality”--Abstract, page iii.


Samaranayake, V. A.

Committee Member(s)

Thimgan, Matthew S.
Wen, Xuerong Meggie


Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics


Master of Science in Applied Mathematics with Statistics Emphasis


Missouri University of Science and Technology

Publication Date

Summer 2020


ix, 36 pages

Note about bibliography

Includes bibliographic references (pages 34-35).


© 2020 Quang Nghia Le, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11879