Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty. This research aims at Bayesian network structure learning and how the learned model can be used for reasoning. Learning the structure of Bayesian network from data is…

When we read, errors in oculomotor programming can cause the eyes to land and fixate on different words from what the mind intended. Previous work suggests that these "mislocated fixations" form 10-30% of first-pass fixations in reading eye movement data, which presents theoretical and analytic issues for eyetracking-while-reading…

In educational contexts, students often self-select into specific interventions (e.g., courses, majors, extracurricular programming). When students self-select into an intervention, systematic group differences may impact the validity of inferences made regarding the effect of the intervention. Propensity score methods are commonly used to reduce…

There has been some research in the use of propensity scores in the context of measurement error in the confounding variables; one recommended method is to generate estimates of the mis-measured covariate using a latent variable model, and to use those estimates (i.e., factor scores) in place of the covariate. I describe a simulation study…

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

The current Situational Space Awareness (SSA) is faced with a huge task of tracking the increasing number of space objects. The tracking of space objects requires frequent and accurate monitoring for orbit maintenance and collision avoidance using methods for statistical orbit determination. Statistical orbit determination enables us to obtain…

Parceling is referred to as a procedure for computing sums or average scores across multiple items. Parcels instead of individual items are then used as indicators of latent factors in the structural equation modeling analysis (Bandalos 2002, 2008; Little et al., 2002; Yang, Nay, & Hoyle, 2010). Item parceling may be applied to alleviate some…

The importance of subscores in educational and psychological assessments is undeniable. Subscores yield diagnostic information that can be used for determining how each examinee's abilities/skills vary over different content domains. One of the most common criticisms about reporting and using subscores is insufficient reliability of subscores.…

This dissertation is composed of three distinct chapters, each of which addresses issues of estimating treatment effects. The first chapter empirically tests the Value-Added (VA) model using school lotteries. The second chapter, co-authored with Michael Wood, considers properties of inverse probability weighting (IPW) in simple treatment effect…

Though the role of traveler information in transportation has been widely recognized in Activity Analysis research, the needs of specific populations receive limited attention. The commuting burden on community college students received comparatively little attention, despite first semester attrition rates and sharp declines in observed parking…

Many nursing programs use standardized testing packages in order to evaluate students' content mastery as well as predict probability of passing the National Council Licensure for Registered Nurses (NCLEX-RN). Instead of a diagnosis for weak content areas, programs implement testing policies in the belief that such policies ensure student success…

This dissertation addresses the use and modeling of spatio-temporal data for the purposes of providing applications for location based services. One of the major issues in dealing with spatio-temporal data for location based services is the availability and sparseness of such data. Other than the hardware costs associated with collecting movement…

In educational research, a randomized controlled trial is the best design to eliminate potential selection bias in a sample to support valid causal inferences, but it is not always possible in educational research because of financial, ethical, and logistical constrains. One alternative solution is use of the propensity score (PS) methods.…

Generalized fiducial inference is a powerful tool for many difficult problems. Based on an extension of R. A. Fisher's work, we used generalized fiducial inference for two extreme value problems and a multiple comparison procedure. The first extreme value problem is dealing with the generalized Pareto distribution. The generalized Pareto…

Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…