The current study investigates the influence of auditory cues on the localization of briefly presented peripheral visual stimuli. Because the brief presentation of peripheral visual stimuli often leads to mislocalization (Binda, Morrone, & Burr, 2010; Bocianski, Musseler, & Erlhagen, 2008; Musseler, Heijden, Mahmud, Dubel, & Ertsey, 1999) these types of stimuli are the most commonly studied and represent the basis of the current study. Musseler et al. (1999) found that peripheral mislocalization toward the fovea occurred during asynchronous presentations of a pair of visual stimuli in retinal periphery, but not during synchronous presentations of stimuli. The current project is an investigation of how sound influences mislocalization of briefly presented peripheral stimuli. If the mechanism of mislocalization is an increased variability of responses when the peripheral stimuli are presented asynchronously, could sound reduce the variability of localization judgments and thus, reduce or eliminate the mislocalization effect? Does sound influence peripheral mislocalization in some other way? This study found that during a relative judgment task, a brief, laterally presented sound leads to mislocalization of a target stimulus toward the direction of the sound (Experiment 1). During an absolute judgment task, however, the influence of the brief, laterally presented sound no longer evokes mislocalization in the direction of the sound. Rather, stimulus onset asynchrony elicits mislocalization similar to the results of Musseler et al. (Experiment 2). When a dynamic sound stimulus occurs prior to the onset of the target stimulus during an absolute judgment task, however, sound idiosyncratically influences the localization of a target stimulus toward the onset of the sound stimulus or direction of the apparent motion of the sound stimulus (Experiment 3).

This thesis consists of three chapters on the statistical adjustment, calibration, and uncertainty quantification of complex computer models with applications in engineering. The first chapter systematically develops an engineering-driven statistical adjustment and calibration framework, the second chapter deals with the calibration of potassium current model in a cardiac cell, and the third chapter develops an emulator-based approach for propagating input parameter uncertainty in a solid end milling process. Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. In the first chapter, we propose a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micromachining process and a model for predicting the temperature of outlet air in a fluidized-bed process. The second chapter studies the calibration of a computer model of potassium currents in a cardiac cell. The computer model is expensive to evaluate and contains twenty-four unknown parameters, which makes the calibration challenging for the traditional methods using kriging. Another difficulty with this problem is the presence of large cell-to-cell variation, which is modeled through random effects. We propose physics-driven strategies for the approximation of the computer model and an efficient method for the identification and estimation of parameters in this high-dimensional nonlinear mixed-effects statistical model. Traditional sampling-based approaches to uncertainty quantification can be slow if the computer model is computationally expensive. In such cases, an easy-to-evaluate emulator can be used to replace the computer model to improve the computational efficiency. However, the traditional technique using kriging is found to perform poorly for the solid end milling process. In chapter three, we develop a new emulator, in which a base function is used to capture the general trend of the output. We propose optimal experimental design strategies for fitting the emulator. We call our proposed emulator local base emulator. Using the solid end milling example, we show that the local base emulator is an efficient and accurate technique for uncertainty quantification and has advantages over the other traditional tools.

Explaining a phenomenon often requires identification of an underlying relationship between two variables. However, it is common practice in psychological research to sample only a few values of an independent variable. Young, Cole, and Sutherland (2012) showed that this practice can impair model selection in between-subject designs. The current study expands that line of research to within-subjects designs. In two Monte Carlo simulations, model discrimination under systematic sampling of 2, 3, or 4 levels of the IV was compared with that under random uniform sampling and sampling from a Halton sequence. The number of subjects, number of observations per subject, effect size, and between-subject parameter variance in the simulated experiments were also manipulated. Random sampling out-performed the other methods in model discrimination with only small, function-specific costs to parameter estimation. Halton sampling also produced good results but was less consistent. The systematic sampling methods were generally rank-ordered by the number of levels they sampled.

Reduced eye fixation and deficits in emotion identification accuracy have been commonly reported in individuals with autism spectrum disorder (AS), but are not ubiquitous. There is growing evidence that emotion processing deficits may be better accounted for by comorbid alexithymia (i.e., difficulty understanding and describing one's emotional state), rather than AS symptoms per se. Another possible explanation is anxiety, which is often comorbid with AS; emotion processing difficulties, including attentional biases, have also been observed in anxiety disorders, suggesting that anxiety symptoms may also influence emotion processing within AS. The purpose of the current study was to test the role of dimensional symptoms of autism, anxious apprehension (AA), and alexithymia in mediating eye fixation across two different facial processing tasks with three adult samples: adults diagnosed with autism (AS; n = 30), adults with clinically-elevated anxiety without autism (HI-ANX; n = 29), and neurotypical adults without high anxiety (NT; n = 46). Experiment 1 involved participants completing an emotion identification task involving short video clips. Experiment 2 was a luminescence change detection task with an emotional-expression photo paired with a neutral-expression photo. Joy, anger, and fear video and photo stimuli were used. Dimensional, mixed-effects models showed that symptoms of autism, but not alexithymia, predicted lower eye fixation across two separate face processing tasks. There were no group differences or significant dimensional effects for accuracy. Anxious apprehension was negatively related to response time in Experiment 1 and positively related to eye fixation in Experiment 2. An attentional avoidance of negative emotions was observed in the NT and HI-ANX group, but not the AS group. The bias was most pronounced at lower levels of AS symptoms and higher levels of AA symptoms. The results provide some evidence for a possible anxiety-related subtype in AS, with participants endorsing high autism symptoms, but low anxious apprehension, demonstrating more classic emotion processing deficits of reduced eye fixation.

<div>Prediction of player performance is a key component in the construction of baseball team rosters. Traditionally, the problem of predicting seasonal plate appearance outcomes has been approached univariately. That is, focusing on each outcome separately rather than jointly modeling the collection of outcomes. More recently, there has been a greater emphasis on joint modeling, thereby accounting for the correlations between outcomes. However, most of these state of the art prediction models are the proprietary property of teams or industrial sports entities and so little is available in open publications.</div><div><br></div><div>This dissertation introduces a joint modeling approach to predict seasonal plate appearance outcome vectors using a mixed-effects multinomial logistic-normal model. This model accounts for positive and negative correlations between outcomes both across and within player seasons. It is also applied to the important, yet unaddressed, problem of predicting performance for players moving between the Japanese and American major leagues.</div><div><br></div>This work begins by motivating the methodological choices through a comparison of state of the art procedures followed by a detailed description of the modeling and estimation approach that includes model t assessments. We then apply the method to longitudinal multinomial count data of baseball player-seasons for players moving between the Japanese and American major leagues and discuss the results. Extensions of this modeling framework to other similar data structures are also discussed.<br>

Criminology’s most recent theoretical tradition involves examination of the developmental onset, continuity, and desistance from offending behavior across the life course. A prominent life course perspective organized around social bonding was proffered by Robert J. Sampson and John H. Laub in dual volumes that include Crime in the Making: Pathways and Turning Points Through Life (1993), and Shared Beginnings, Divergent Lives (2003). Because Sampson and Laub’s age-graded theory is based on a sample of White males born in the 1920s and 1930s, and matured during a historical period of vast economic growth, the universal theoretical processes emphasized in their theory may be overstated. Such assumptions may not generalize to more heterogeneous samples that includes minorities and individuals that vary in their levels of offending. The present research evaluates the generalizability of the age-graded theory through examination of data collected from a representative and contemporary sample of adolescents followed into adulthood. In addition, this study seeks to examine an alternate turning point from deviant conduct, specifically religiosity/spirituality. Building on prior studies that explore the role of religiosity on change processes across race and ethnicity (Chu & Sung, 2009; Stansfield, 2017), the current investigation addresses open questions relating to the nature of the religion-desistance relationship. Multilevel mixed effects models are utilized to estimate over time the separate impact of religious behavior and religious beliefs on deviant conduct, to further assess a religious turning point effect across subgroups disaggregated by race/ethnicity, and to evaluate the influence of religiosity on change from deviant outcomes characterized as violations of secular and ascetic standards. Analyses of religiosity/spirituality on these differing forms of deviance across race/ethnicity are also conducted. In contrast to the hypothesized relationships, study findings reveal very little evidence of a religious/spiritual turning point effect in enacting change from deviant behaviors in the main models. Similar results indicate that religiosity indicates minimal differences in change from deviant conduct when the sample is disaggregated across race and ethnicity. Findings point to the nuances of the religion-desistance relationship, and depends upon processes that may involve attendance to church services or spiritual beliefs, and may be conditional on the type of deviance outcome examined—whether in violation of a secular or ascetic standard. Along with a discussion of these findings, limitations of the study, directions for future research, and implications for policy are provided.

Vegetative health was measured both before and after Hurricane Harvey using remotely sensed vegetation indices on the coastal marshland surrounding Galveston Island's West Bay. Data were recorded on a monthly basis following the hurricane from September of 2005 until September of 2019 in order to document the vegetation response to this significant disturbance event. Both initial impact and recovery were found to be dependent on a variety of factors, including elevation zone, spatial proximity to the bay, the season during which recovery took place, as well as the amount of time since the hurricane. Slope was also tested as a potential variable using a LiDAR-derived slope raster, and while unable to significantly explain variations in vegetative health immediately following the hurricane, it was able to explain some degree of variability among spatially close data points. Among environmental factors, elevation zone appeared to be the most key in determining the degree of vegetation impact, suggesting that the different plant assemblages that make up different portions of the marsh react differently to the severe flooding that took place during Harvey.

It is well established in the literature that children with developmental coordination disorder (DCD) are more likely to be physically inactive, have unhealthy weight, and report lower perceptions of self-worth than typically developing (TD) children. Physical inactivity, overweight/obesity and low self-worth are important risk factors for many physical and psychological health conditions. The interrelationships among these factors, however, have yet to be explored in children with DCD. There is limited information on change in body composition measures and self-worth over time in children with DCD, the effect of physical activity (PA) on body composition, and whether the combined negative influence of having both DCD and obesity result in poorer conceptions of self-worth. In this dissertation, I present a series of studies that explore the connections among these factors using longitudinal, population-based data on a large cohort of children with and without poor motor coordination. The first study, presented in Chapter 2, describes the results of change in BMI and waist circumference (WC) in children with probable DCD (pDCD) and TD children over a five-year time period, and the effects of sex and PA on this relationship. Chapter 3 describes the results of the relationship between body fat, pDCD, and PA after addressing the measurement- related limitations of the study reported in Chapter 2. Chapter 4 describes the results of self-worth in children with pDCD and overweight/obesity, only pDCD, only overweight/obesity, and the control group at baseline as well as change over time. Collectively, the results show that children with pDCD have a consistently higher BMI, WC, and body fat than TD children. BMI and WC increases over time in children with pDCD; specifically boys with pDCD show a much accelerated increase in these measures. Scores of body composition measures increase with decrease in self-reported and objectively measured PA, but participation in PA does not explain why children with pDCD are more likely to have excess weight gain. Finally, children with both pDCD and overweight/obesity and children with either of these conditions alone report lower self- worth than the control group, and the change in self-worth between groups remains constant over time. / Dissertation / Doctor of Philosophy (PhD)

Emergency departments are fundamental for providing high-quality care, and their operations directly impact the logistics of the hospitals in their entirety. Poor emergency department performance leads to delays, prolonged hospitalization, and improper allocation of resources, reducing the quality of the provided care and increasing costs. Describing the variability embedded in real clinical data in a useful way is essential for improving the organization of hospitals in the near future. However, it is a challenging task due to clinical complexity and the lack of an established bridge between logistic systems and the clinical insights of the hospital. Therefore, this work aims to design and implement a simplified process model describing patient flow within an emergency department, which could allow the evaluation of the clinical impact of complex patient characteristics on the system's logistics. To achieve this, a novel nonlinear mixed-effects approach with hospital medical records was applied to design patient flow within the emergency department in the form of a multi-state Markov process. Four independent training data samples were extracted from the main dataset. For each of them, the set of covariates that could lead to the most significant improvement in the values of the employed likelihood indicators was selected. Through statistical tests, analysis of the outputs, and a validation process carried out on a fifth and independent dataset, it was possible to obtain a final model containing the most relevant and significant covariates for describing each of the modeled state transitions and confirming their clinical meaningfulness and relevance. The results achieved in this thesis can lead to future improvement of the healthcare logistics systems by extending the use of nonlinear mixed-effects approaches to the estimation of the covariate impact on emergency department flows. / Akutmottagningar är centrala för att tillhandahålla högkvalitativ vård. Deras verksamhet har en direkt inverkan på sjukhusens logistik i helhet. Undermålig prestation i en akutmottagning leder till förseningar, förlängd sjukhusvistelse för patienter och olämpliga resursfördelningar, som i sin tur försämrar kvaliteten på den erbjudna vården, samt ökar kostnader. Därför är det viktigt att beskriva den variabilitet som är inbäddad i kliniskt data för att kunna förbättra strukturen av sjukhus i den närmaste framtiden. Emellertid är det ett utmanande uppdrag på grund av den kliniska komplexiteten och bristen på en etablerad bro mellan logistiska system och insikter om den kliniska situationen på sjukhuset. Detta examensarbete ämnar därför designa och implementera en förenklad processmodel som beskriver patientflödet inom en akutmottagning, vilket skulle kunna tillåta evaluering av vad för klinisk inverkan patienters komplexa egenskaper har på systemets logistik. För att uppnå detta tillämpades ett nytt icke-linjärt tillvägagångssätt för blandade effekter med patientjournaler, med syfte att designa patientflöde inom akutmottagningen i form av en Markovprocess i flera tillstånd. Fyra oberoende urvalsgrupper med övningsdata extraherades från huvuddatasetet och för var och en av dem valdes den uppsättning kovariat som hade möjlighet att leda till den största förbättringen i de applicerade sannolikhetsindikatorerna. Genom statistiska test, analys av uteffekten och en valideringsprocess utförd på en femte oberoende urvalsgrupp, möjliggjordes framtagandet av en slutgiltig modell innehållande de mest relevanta och signifikanta kovariat för att beskriva var och en av de modellerade tillståndsövergångarna, och bekräfta dess kliniska betydelse och relevans. De resultat som uppnåddes i det här examensarbetet har potential att i framtiden leda till förbättring av sjukvårdens logistiksystem, genom att utvidga användningen av icke-linjära blandade effektmodeller för att uppskatta kovariatinverkan på akutmottagningsflöden.