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Dysfunctions of ocular motor and binocular vision are some of the most commonly observed problems in patients with severe traumatic brain injury. Secondarily, subjective complaints of compromised vision and ocular motor functions are also sometimes reported in mild traumatic brain injuries (mTBI). Simple ocular/vision assessments such as near point of convergence (NPC), the King-Devick Test (KDT), and stereoacuity can be performed to identify and assess these deficits, but their diagnostic accuracy has yet to be thoroughly investigated. The purpose of this study was to determine if differences exist in NPC, KDT, and stereoacuity test scores between concussed and control athletes, and to determine the diagnostic accuracy of these tests. A multicenter control group design was utilized. The independent variable was group (control vs. concussed). The dependent variables were the ocular test scores from the NPC, KDT, and stereoacuity tests. Participants were recruited from several collegiate athletic programs. In total 34 healthy, non-concussed controls (21 male, 13 female) aged 19 + 1.5 years and 19 concussions (11 male, 8 female) aged 20.42 + 1.5 years participated in the study. A concussion was operationally defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces, that was diagnosed by a health care professional through the use of signs and symptoms scales, balance and neurocognitive testing. Data were analyzed using descriptive and inferential statistics. T-tests and chi-squares were performed to ensure there were no significant differences between groups on specific demographic or relevant prognostic variables (sport, sex, and concussion history). T-tests were employed to identify significant differences between groups on ocular test scores. Then clinical and statistical cutoffs for all three tests were determined. Based off of these cutoffs sensitivity, specificity, and likelihood ratios were determined for each assessment. Furthermore, receiver operating characteristic (ROC) curves were calculated to help determine the diagnostic accuracy of these assessments. The alpha level was set at p < .05 and the SPSS for Windows, Version 21.0, statistical program (IBM, Inc., Armonk, NY) was used for all data analysis. Significant differences were found between groups for all three ocular assessments. NPC demonstrated a statistical cutoff of 5.5 cm, which provided a sensitivity of 79% and specificity of 76% and an AUC of 0.827. For the KDT, a statistical cutoff time of 49.5s demonstrated a sensitivity of 58% and specificity of 72% with an AUC of 0.658. Lastly, for stereoacuity a statistical cutoff point of 28.50 arc sec was found which produced a sensitivity of 65% and specificity of 54% with a maximum AUC of 0.706. All three tests demonstrated their potential to positively contribute to the diagnosis of a concussion. / Kinesiology

Since the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, ƒ, and the nonparametric function, 𝑔, were combined in a straightforward fashion via the use of a mixing parameter, λ. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch developed the MRR2 estimate as an alternative to MRR1. This model involved first forming the parametric fit to the data, and then adding in an estimate of 𝑔 according to the lack of fit demonstrated by the error terms. Using small samples, they illustrated the superiority of MRR2 to MRR1 in most situations. In this dissertation we have developed asymptotic convergence rates for both MRR1 and MRR2 in OLS and GLS (maximum likelihood) settings. In many of these settings, it is demonstrated that the user of MRR1 or MRR2 achieves the best convergence rates available regardless of whether or not the model is properly specified. This is the "Golden Result of Model Robust Regression". It turns out that the selection of the mixing parameter is paramount in determining whether or not this result is attained. / Ph. D.

Classical adaptive signal processors typically utilize assumptions in their derivation. The presence of adequate Gaussian and independent and identically distributed (i.i.d.) input data are central among such assumptions. However, classical processors have a tendency to suffer a degradation in performance when assumptions like these are violated. Worse yet, such degradation is not guaranteed to be proportional to the level of deviation from the assumptions. This dissertation proposes new signal processing algorithms based on aspects of modern robustness theory, including methods to enable adaptivity of presently non-adaptive robust approaches. The contributions presented are the result of research performed jointly in two disciplines, namely robustness theory and adaptive signal processing. This joint consideration of robustness and adaptivity enables improved performance in assumption-violating scenarios—scenarios in which classical adaptive signal processors fail. Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for high-dimension data is developed and shown robust in problematic contamination. Second, a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally, a new suppression-based robust estimator is developed for use with complex-valued adaptive signal processing data. To exercise the proposals and compare their performance to state- of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive Processing (STAP) radar data, both real and simulated, are processed, and performance is subsequently computed and displayed. The new algorithms are shown to outperform their state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) convergence rate and target detection perspective. / Ph. D.

This dissertation presents a new framework for the development and analysis of blind adaptive algorithms. An adaptive algorithm is said to be 'blind' if it does not require a known training sequence. The main focus is on application of these algorithms to adaptive antenna arrays in mobile radio communications. Adaptive antenna arrays can reduce the effects of cochannel interference, multipath fading, and background noise as compared to more conventional antenna systems. For these reasons, the use of adaptive antennas in wireless communication has received a great deal of attention in the literature. There are several reasons why the study of blind adaptive algorithms is important. First, it is common practice to switch to a blind mode once the training sequence has been processed in order to track a changing environment. Furthermore, the use of a blind algorithm can completely eliminate the need for a training sequence. This is desirable since the use of a training sequence reduces the number of bits available for transmitting information. The analysis framework introduced here is shown to include the well-known Constant Modulus Algorithm (CMA) and decision directed algorithm (DDA). New results on the behavior of the CMA and DDA are presented here, including analytic results on the convergence rate. Previous results have relied on Monte Carlo simulation. This framework is also used to propose a new class of blind adaptive algorithms that offer the potential for improved convergence rate. / Ph. D.