Browsing by Subject "prediction"
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Item Assessment of Driving Mental Models as a Predictor of Crashes and Moving Violations(2012-07-16) Munoz Galvez, Gonzalo JavierThe purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge?such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = ?.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.Item Multi-step-ahead prediction of MPEG-coded video source traffic using empirical modeling techniques(Texas A&M University, 2006-04-12) Gupta, DeepankerIn the near future, multimedia will form the majority of Internet traffic and the most popular standard used to transport and view video is MPEG. The MPEG media content data is in the form of a time-series representing frame/VOP sizes. This time-series is extremely noisy and analysis shows that it has very long-range time dependency making it even harder to predict than any typical time-series. This work is an effort to develop multi-step-ahead predictors for the moving averages of frame/VOP sizes in MPEG-coded video streams. In this work, both linear and non-linear system identification tools are used to solve the prediction problem, and their performance is compared. Linear modeling is done using Auto-Regressive Exogenous (ARX) models and for non linear modeling, Artificial Neural Networks (ANN) are employed. The different ANN architectures used in this work are Feed-forward Multi-Layer Perceptron (FMLP) and Recurrent Multi-Layer Perceptron (RMLP). Recent researches by Adas (October 1998), Yoo (March 2002) and Bhattacharya et al. (August 2003) have shown that the multi-step-ahead prediction of individual frames is very inaccurate. Therefore, for this work, we predict the moving average of the frame/VOP sizes instead of individual frame/VOPs. Several multi-step-ahead predictors are developed using the aforementioned linear and non-linear tools for two/four/six/ten-step-ahead predictions of the moving average of the frame/VOP size time-series of MPEG coded video source traffic. The capability to predict future frame/VOP sizes and hence the bit rates will enable more effective bandwidth allocation mechanism, assisting in the development of advanced source control schemes needed to control multimedia traffic over wide area networks, such as the Internet.Item Predicting Couple Therapy Dropouts in Veteran Administration Medical Centers(2012-10-19) Hsueh, AnnieThe present study examined predictors of couple therapy dropout in the VA medical centers using six different dropout criteria. The most accurate dropout definitions included using a statistical modeling procedure to determine whether the client's rate of change at the final session was greater than average of change for all clients; clients who were still demonstrating gains greater than the average rate of change at the final session were considered to have terminated prematurely. A total of 177 couples (354 individuals) who sought therapy in the VA medical centers in Charleston, SC and San Diego, CA were examined. With a few exceptions, demographic variables generally did not predict dropout. A couple's relationship adjustment and response to conflict were significant predictors of dropout. The content of therapy sessions predicted dropout only when dropout was defined, at least in part, by client's rate of change at the final session, suggesting that such methods of defining premature termination are the most sensitive to the therapy process. Therapists' characteristics, including gender and level of experience, did not predict dropout across all six definitions of dropout.Item Three essays on business failure: causality and prediction(2009-05-15) Zhang, JinThis dissertation investigates three issues on business failure causality and prediction. First, a nonlinear model for mathematical programming based discriminant analysis is studied. This study proposes a nonlinear model that builds on the existing linear and quadratic models and allows for a more flexible degree of nonlinearity through a set of power parameters. The proposed nonlinear model is solved using a genetic algorithm and is tested against linear and quadratic models using real financial data. The results show that each model is better in certain cases, but the nonlinear model turns out to be the best overall among the three. Better performance of this nonlinear model appears likely, but a more robust solver would be required. Second, the relationship between aggregate business failures and macroeconomic conditions is studied from a causality perspective. A structural Vector Autoregression (VAR) is used while incorporating the recently developed causal inference method Directed Acyclic Graph (DAG). Particularly, DAG is used to provide a contemporaneous causal structure and the VAR results are summarized using innovation accounting techniques. The results show that during the period from 1980 to 2004 in the U.S., aggregate business failures were influenced by interest rates, but overall these failures appear to be far more exogenous than was found previously. Third, the effect of incorporating macroeconomic variables into business failure prediction models is investigated with a focus on the U.S. airline industry from 1995 to 2005. The attention is placed on prediction accuracy, parameter stability, and the effect of particular macroeconomic variables. The results show that the stability of parameters in the prediction model is improved when macro variables are added. In terms of prediction accuracy, the model augmented with a macro variable performed better in a jackknife prediction, but not in out-of-sample predictions. The macroeconomic variable found to be significant is the change of interest rate, which is probably related to the high level of leverage common in this particular industry. Also, the results demonstrate that a probability score can be used as a more informative evaluation measure than the current one based on cutoff probabilities.Item Utilizing body temperature to evaluate ovulation in mature mares(Texas A&M University, 2006-08-16) Bowman, Marissa CoralThe equine breeding industry continues to be somewhat inefficient, even with existing technology. On average, foaling rates are low when compared with that of other livestock. One major contributor is the inability to accurately predict ovulation in mares, which ovulate before the end of estrus, leaving much variability in coordinating insemination. A more efficient, less invasive method that could replace or reduce the need for constant teasing and ultrasonography to evaluate follicular activity is needed. In both dairy cattle and women, a change in body temperature has been shown to occur immediately prior to ovulation. Research on horses has been limited, although one study reported no useable relationship between body temperature and ovulation in mares (Ammons, 1989). The current study utilized thirty-eight mature cycling American Quarter Horse mares, and was conducted from March-August 2004. Each mare was implanted in the nuchal ligament with a microchip that can be used for identification purposes, but is also capable of reporting body temperature. Once an ovulatory follicle (>35mm) was detected using ultrasonography and the mare was exhibiting signs of estrus, the mare's follicle size and temperature were recorded approximately every six hours until ovulation. Not only was the temperature collected using the microchips, but the corresponding rectal temperature was also recorded using a digital thermometer. A significant effect (p<0.05) on body temperature was noted in relation to the presence or absence of an ovulatory follicle (>35mm) under different circumstances. When evaluating the rectal temperatures, no significant difference was found in temperature in relation to the presence or absence of a follicle. However, in the temperatures obtained using the microchip, temperature was higher (p<0.05) with the presence of a follicle of greater than 35mm. This may be due to the extreme sensitivity of the microchip implant and its ability to more closely reflect minute changes in body temperature.