Performance is key to survival. From day-to-day foraging events, to reproductive activities, to life-or-death crises, how well an organism performs these tasks can determine success or failure. Selection, therefore, both natural and sexual, act upon performance, and performance demands on individuals shape a population’s morphological and physiological trait distributions. While studies of morphological adaptations to ecological pressures implicitly center on the idea that responses to selection improve performance via changes in morphology, the relationships between morphology, performance, and fitness are not always well understood. In this dissertation, I investigate these relationships explicitly, as well as determine the effects that different ecological and genetic contexts have on selection and how populations respond to performance pressures. Using a model of lizard locomotor performance, I address three issues that may impact selection on performance that are often overlooked in performance studies. First, performance is not a static trait. Rather, individuals possess a range of performance abilities or intensities that can be expressed as needed. Using a novel, individual-based, quantitative genetic simulation model, I demonstrate the effects of variable performance expression and genetic constraints on how a population experiences and responds to selection on sprint and endurance performance. Second, sex differences in performance are expected in sexually dimorphic species, but empirical evidence for this is lacking. To this end, I measured and analyzed multivariate morphology and performance in Anolis carolinensis to identify sex-specific patterns in functional morphology and functional trade-offs within a broad suite of performance traits. Third, intralocus sexual conflict should constrain the evolution of the multivariate performance phenotype in both sexes. By extending the simulation model to include correlated trait inheritance between sexes and sex-specific selection on certain performance traits, I demonstrate the extent to which this sexual conflict constrains performance evolution. In combining studies of natural populations with simulation studies of selection, this dissertation embraces the complexity of performance to address the multiple contributing factors and constraints on performance evolution, and demonstrates the importance of accounting for such complexity when studying animal performance.

Previous solutions for the the Law of Categorical Judgment with category boundary variability have either constrained the standard deviations of the category boundaries in some way or have violated the assumptions of the scaling model. In the current work, a fully Bayesian Markov chain Monte Carlo solution for the Law of Categorical Judgment is given that estimates all model parameters (i.e. scale values, category boundaries, and the associated standard deviations). The importance of measuring category boundary standard deviations is discussed in the context of previous research in signal detection theory, which gives evidence of interindividual variability in how respondents perceive category boundaries and even intraindividual variability in how a respondent perceives category boundaries across trials. Although the measurement of category boundary standard deviations appears to be important for describing the way respondents perceive category boundaries on the latent scale, the inclusion of category boundary standard deviations in the scaling model exposes an inconsistency between the model and the rating method. Namely, with category boundary variability, the scaling model suggests that a respondent could experience disordinal category boundaries on a given trial. However, the idea that a respondent actually experiences disordinal category boundaries seems unlikely. The discrepancy between the assumptions of the scaling model and the way responses are made at the individual level indicates that the assumptions of the model will likely not be met. Therefore, the current work examined how well model parameters could be estimated when the assumptions of the model were violated in various ways as a consequence of disordinal category boundary perceptions. A parameter recovery study examined the effect of model violations on estimation accuracy by comparing estimates obtained from three response processes that violated the assumptions of the model with estimates obtained from a novel response process that did not violate the assumptions of the model. Results suggest all parameters in the Law of Categorical Judgment can be estimated reasonably well when these particular model violations occur, albeit to a lesser degree of accuracy than when the assumptions of the model are met.

This thesis discusses the changes in parameters of linear models and methods of their detection. It begins with a short introduction of the two basic types of change point detection procedures and bootstrap algorithms developed specifically to deal with dependent data. In the following chapter we focus on the location model - the simplest example of a linear model with a change in parameters. On this model we will illustrate a way of long-run variance estimation and implementation of selected bootstrap procedures. In the last chapter we show how to extend the applied methods to linear models with a change in parameters. We will compare the performance of change point tests based on asymptotic and bootstrap critical values through simulation studies in both our considered methods. The performance of selected long-run variance estimator will also be examined both for situations when the change in parameters occurs and when it does not. 1

Title: Tests of independence for multivariate data Author: Bc. Michal Kudlík Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Marek Omelka, PhD., Department of Probability and Mathema- tical Statistics Abstract: This thesis is an overview of tests of independence for multidimensi- onal data. The report includes tests on independence of categorical and conti- nuous random variables, tests assuming normal distribution of data, asymptotic nonparametric tests and permutation tests with application of the Monte Carlo method. This thesis shows the suitability of tests with properly chosen real data and checks significance level and compares the strength of the selected tests by simulation study while using appropriate statistical software. Based on the simu- lation study the thesis discusses an appropriateness of the use of different tests for different situations. Keywords: independence, permutation and asymptotic tests of independence, Monte Carlo method, simulation study 1

The objective of research is to develop a computer simulation modeltoprovide a means to effectively and efficiently reduce medication errors associated with points of distribution sitesby identifying and manipulating screeners with a high probability of generating errors.Points of distribution sites are used to rapidly distribute chemoprophylaxis to a large population in response to a pandemic event or a bioterrorism attack. Because of the nature of therapid response, points of distribution sites require the use of peer-trained helpers who volunteer their services.The implications are that peer-trained helperscould have a variety of experience or education levels. Thesefactors increase the risk of medical errors. Reducing medical errors is accomplished through changing the means in which healthcare providers are trained and focusing on a team approach to healthcare delivery. Computer simulations have been used in the past to identify sources of inefficiency and potential of error. Data for the model werecollected over the course of two semesters. Of the 349 data points collected from the first semester, only 137 data points were usable for the purposes of modelbuilding. When the experiment was conducted again for the second semester, similar results werefound. The control simulation was run 20 times with each screener generating errors with a probability of 0.101 following a Bernoulli distribution. The variable simulation was run 30 times with each screener generating the same probability of errors; however, the researcher identified the screeners generating the errors and immediately stopped them from processing additional agents once they reached five errors. An ANOVA was conducted on the percent errors generated from each simulation run. The results of the ANOVA showedsignificant difference between individuals within the groups. A simulation model wasbuilttoreflect the differences in medical error rates between screeners. By comparing the results of the simulation as the screeners are manipulated in the system, the model can be used to show how medical errors can be reduced in points of distribution sites

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Approved for public release; distribution in unlimited. / Budget limitations have reduced the number of military components available for testing, and time constraints have reduced the amount of time available for actual testing resulting in many items still operating at the end of test cycles. These two factors produce small test populations (small sample size) with "heavily" censored data. The assumption of "normal approximation" for estimates based on these small sample sizes reduces the accuracy of confidence bounds of the probability plots and the associated quantities. This creates a problem in acquisition analysis because the confidence in the probability estimates influences the number of spare parts required to support a mission or deployment or determines the length of warranty ensuring proper operation of systems. This thesis develops a method that simulates small samples with censored data and examines the error of the Fisher-Matrix (FM) and the Likelihood Ratio Bounds (LRB) confidence methods of two test populations (size 10 and 20) with three, five, seven and nine observed failures for the Weibull distribution. This thesis includes a Monte Carlo simulation code written in S-Plus that can be modified by the user to meet their particular needs for any sampling and censoring scheme. To illustrate the approach, the thesis includes a catalog of corrected confidence bounds for the Weibull distribution, which can be used by acquisition analysts to adjust their confidence bounds and obtain a more accurate representation for warranty and reliability work. / Civilian, Department of the Air Force

This thesis shows that consumers' behaviour has a decisive impact on the innovative behaviour of firms and on the development of industry. As a framework, an evolutionary simulation model is chosen, and market interactions are modelled according to a search theoretic approach.

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.

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