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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Language
  • English
Resource Type
  • Masters Thesis
  • 1 Text
Subject
Date Range
2011 2020


In many classication problems data samples cannot be collected easily, example in drug trials, biological experiments and study on cancer patients. In many situations the data set size is small and there are many outliers. When classifying such data, example cancer vs normal patients the consequences of mis-classication are probably more important than any other data type, because the data point could be a cancer patient or the classication decision could help determine what gene might be over expressed and perhaps a cause of cancer. These mis-classications are typically higher in the presence of outlier data points. The aim of …

Contributors
Gupta, Sidharth, Kim, Seungchan, Welfert, Bruno, et al.
Created Date
2011

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively …

Contributors
Venkatesan, Ashok, Panchanathan, Sethuraman, Li, Baoxin, et al.
Created Date
2011

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for …

Contributors
Thulasiram, Ramesh L., Ye, Jieping, Xue, Guoliang, et al.
Created Date
2011

The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth guidelines regarding the minimum sample size and ideal number and quality of indicators. In particular, we studied to which extent including a larger number of high quality indicators can compensate for a small sample size in LCA. The results suggest that in general, larger sample size, more indicators, higher quality of indicators, and a larger covariate effect correspond to more converged and proper replications, as well as fewer boundary estimates …

Contributors
Wurpts, Ingrid Carlson, Geiser, Christian, Aiken, Leona, et al.
Created Date
2012

Photovoltaic (PV) modules are typically rated at three test conditions: STC (standard test conditions), NOCT (nominal operating cell temperature) and Low E (low irradiance). The current thesis deals with the power rating of PV modules at twenty-three test conditions as per the recent International Electrotechnical Commission (IEC) standard of IEC 61853 – 1. In the current research, an automation software tool developed by a previous researcher of ASU – PRL (ASU Photovoltaic Reliability Laboratory) is validated at various stages. Also in the current research, the power rating of PV modules for four different manufacturers is carried out according to IEC …

Contributors
Vemula, Meena Gupta, Tamizhmani, Govindasamy, Macia, Narcio F., et al.
Created Date
2012

This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We …

Contributors
Liu, Hui, Taylor, Thomas, Cochran, Douglas, et al.
Created Date
2012

When analyzing longitudinal data it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data is presented. The appropriate and valid estimating equations associated with the time-dependent covariates are identified, thus providing substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. Identifying the estimating equations for computation is of utmost importance. …

Contributors
Yin, Jianqiong, Wilson, Jeffrey Wilson, Reiser, Mark, et al.
Created Date
2012

Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of …

Contributors
Reichenberg, Ray E., Levy, Roy, Thompson, Marilyn S., et al.
Created Date
2013

The objective of this thesis is to investigate the various types of energy end-uses to be expected in future high efficiency single family residences. For this purpose, this study has analyzed monitored data from 14 houses in the 2013 Solar Decathlon competition, and segregates the energy consumption patterns in various residential end-uses (such as lights, refrigerators, washing machines, ...). The analysis was not straight-forward since these homes were operated according to schedules previously determined by the contest rules. The analysis approach allowed the isolation of the comfort energy use by the Heating, Venting and Cooling (HVAC) systems. HVAC are the …

Contributors
Garkhail, Rahul, Reddy, T Agami, Bryan, Harvey, et al.
Created Date
2014

Obtaining high-quality experimental designs to optimize statistical efficiency and data quality is quite challenging for functional magnetic resonance imaging (fMRI). The primary fMRI design issue is on the selection of the best sequence of stimuli based on a statistically meaningful optimality criterion. Some previous studies have provided some guidance and powerful computational tools for obtaining good fMRI designs. However, these results are mainly for basic experimental settings with simple statistical models. In this work, a type of modern fMRI experiments is considered, in which the design matrix of the statistical model depends not only on the selected design, but also …

Contributors
Zhou, Lin, Kao, Ming-hung, Reiser, Mark, et al.
Created Date
2014