My thesis examines nonlinearity between catch and effort. I use a meta-analysis of published literature and generalized linear mixed-effects models (GLMM) on both fixed and mobile gear fisheries of Atlantic Canada. The meta-analysis examines the proportionality of catch to effort using the slope of the reduced major axis (RMA) log-log regression, which accounts for “errors-in-variables”. The GLMMs explored proportionality while accounting for variation among fishing vessels. Both analyses found evidence for disproportionality between catch and effort. Catch that increases disproportionally to effort could result from either facilitation or recruitment of effort into the fishery. Catch increases that are less than proportional are expected from competitive interactions among fishers or gear saturation. The GLMM also revealed that the level of aggregation (by set, trip, monthly, or annually) can affect the apparent proportionality between catch and effort. In general, catch and effort should not be considered to be proportional. / May 2016

Det förefaller tillfredsställande att minska sin osäkerhet. Med tanke på hur generell osäkerhet är som psykologiskt fenomen och hur viktig känslan av tillfredsställelse är som grund för beteende förtjänar sambandet att utredas närmare. Det finns idag ingen tydlig kvantifiering av psykologisk osäkerhet och huruvida grad av minskad osäkerhet predicerar tillfredsställelse är oklart. I denna studie undersöktes sambandet genom ett datoriserat experiment, där deltagarna skattade sin osäkerhet på olika ords betydelser och sedan skattade sin överraskning och tillfredsställelse när de fått veta rätt svar. Experimentet genomfördes på 18 deltagare rekryterade via annonser på universitetet och relaterade hemsidor. I direkt motsats till hypotesen visade resultaten att ju högre den initiala osäkerheten var, desto lägre blev tillfredsställelsen av att eliminera den. Sambandet förklaras av att prestation hade stor betydelse för tillfredsställelse där rätta svar ledde till högre tillfredsställelse och felaktiga svar ledde till lägre tillfredsställelse. Osäkerhet hade inte någon effekt på tillfredsställelse när effekten av prestation kontrollerades för.  Deltagarna besvarade även ett personlighetstest som visade att grad av Neuroticism var relaterat till ett starkare negativt samband mellan tillfredsställelse och lösande av osäkerhet, kontrollerat för prestation. Våra resultat tyder på att upplevda krav på prestation kan överskugga tillfredsställelsen vid lösande av osäkerhet. Effekten av prestation på tillfredsställelse i relation till osäkerhet är inte tidigare utförligt undersökt och mer forskning kan ge ny information om inställningen till inlärning. / It appears satisfying to decrease ones uncertainty. Considering how general uncertainty is as a psychological phenomenon, and how important the sense of satisfaction is as a basis for behavior, this connection deserves to be further examined. As of today, there is no clear quantification of psychological uncertainty, and whether degree of decreased uncertainty predicts satisfaction is unclear. In this study, this connection was examined through a computerized experiment where participants estimated their uncertainty on the meaning of different words and then estimated their surprise and satisfaction when receiving the correct answer. The experiment was performed on 18 participants recruited with posters on campus and related internet sites. Contrary to the hypothesis, the results showed that the higher the initial uncertainty, the lower the satisfaction was when eliminating it. The connection is explained by the impact of performance on satisfaction, where correct answers lead to higher satisfaction and incorrect answers lead to lower satisfaction. Uncertainty had no effect on satisfaction when the effect of performance was accounted for. The participants also answered a personality questionnaire which showed that higher degrees of Neuroticism was related to a stronger negative connection between satisfaction and the resolution of uncertainty, when performance was accounted for. Our results suggest that perceived performance demands may overshadow the satisfaction received when resolving uncertainty. The effect of performance on satisfaction in relation to uncertainty has not been extensively examined and further studies may provide new information about the attitude towards learning.

In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on certain covariates. In fact, in HIV/AIDS research, the mean effect may not always be of interest. Additionally, if obvious outliers or heavy tails exist, mean regression model may lead to non-robust results. Moreover, due to some data features, like left-censoring caused by the limit of detection (LOD), covariates with measurement errors and skewness, analysis of such complicated longitudinal and survival data still poses many challenges. Ignoring these data features may result in biased inference. Compared to the mean regression model, quantile regression (QR) model belongs to a robust model family, which can give a full scan of covariate effect at different quantiles of the response, and may be more robust to extreme values. Also, QR is more flexible, since the distribution of the outcome does not need to be strictly specified as certain parametric assumptions. These advantages make QR be receiving increasing attention in diverse areas. To the best of our knowledge, few study focuses on the QR-based joint models and applies to longitudinal-survival data with multiple features. Thus, in this dissertation research, we firstly developed three QR-based joint models via Bayesian inferential approach, including: (i) QR-based nonlinear mixed-effects joint models for longitudinal-survival data with multiple features; (ii) QR-based partially linear mixed-effects joint models for longitudinal data with multiple features; (iii) QR-based partially linear mixed-effects joint models for longitudinal-survival data with multiple features. The proposed joint models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also implemented to assess the performance of the proposed methods under different scenarios. Although this is a biostatistical methodology study, some interesting clinical findings are also discovered.

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.

Difficulties in speech production are often marked by disfluency; fillers, hesitations, prolongations, repetitions and repairs. In recent years a body of work has emerged that demonstrates that listeners are sensitive to disfluency, and that this affects their expectations for upcoming speech, as well as their attention to the speech stream. This thesis investigates the extent to which delay may be responsible for triggering these effects. The experiments reported in this thesis build on an Event Related Potential (ERP) paradigm developed by Corley et al., (2007), in which participants listened to sentences manipulated by both fluency and predictability. Corley et al. reported an attenuated N400 effect for words following disfluent ers, and interpreted this as indicating that the extent to which listeners made predictions was reduced following an er. In the current set of experiments, various noisy interruptions were added to Corley et al.,'s paradigm, time matched to the disfluent fillers. These manipulations allowed investigation of whether the same effects could be triggered by delay alone, in the absence of a cue indicating that the speaker was experiencing difficulty. The first experiment, which contrasted disfluent ers with artificial beeps, revealed a small but significant reduction in N400 effect amplitude for words affected by ers but not by beeps. The second experiment, in which ers were contrasted with speaker generated coughs, revealed no fluency effects on the N400 effect. A third experiment combined the designs of Experiments 1 and 2 to verify whether the difference between them could be characterised as a context effect; one potential explanation for the difference between the outcomes of Experiments 1 and 2 is that the interpretation of an er is affected by the surrounding stimuli. However, in Experiment 3, once again no effect of fluency on the magnitude of the N400 effect was found. Taken together, the results of these three studies lead to the question of whether er's attenuation effect on the N400 is robust. In a second part to each study, listeners took part in a surprise recognition memory test, comprising words which had been the critical words in the previous task intermixed with new words which had not appeared anywhere in the sentences previously heard. Participants were significantly more successful at recognising words which had been unpredictable in their contexts, and, importantly, for Experiments 1 and 2, were significantly more successful at recognising words which had featured in disfluent or interrupted sentences. There was no difference between the recognition rates of words which had been disfluent and those which were affected by a noisy interruption. Collard et al., (2008) demonstrated that disfluency could raise attention to the speech stream, and the finding that interrupted words are equally well remembered leads to the suggestion that any noisy interruption can raise attention. Overall, the finding of memory benefits in response to disfluency, in the absence of attenuated N400 effects leads to the suggestion that different elements of disfluencies may be responsible for triggering these effects. The studies in this thesis also extend previous work by being designed to yield enough trials in the memory test portion of each experiment to permit ERP analysis of the memory data. Whilst clear ERP memory effects remained elusive, important progress was made in that memory ERPs were generated from a disfluency paradigm, and this provided a testing ground on which to demonstrate the use of linear mixed-effects modelling as an alternative to ANOVA analysis for ERPs. Mixed-effects models allow the analysis of unbalanced datasets, such as those generated in many memory experiments. Additionally, we demonstrate the ability to include crossed random effects for subjects and items, and when this is applied to the ERPs from the listening section of Experiment 1, the effect of fluency on N400 amplitude is no longer significant. Taken together, the results from the studies reported in this thesis suggest that temporal delay or disruption in speech can trigger raised attention, but do not necessarily trigger changes in listeners' expectations.

The measurement of human immunodeficiency virus ribonucleic acid levels over time leads to censored longitudinal data. Suitable models for dynamic modelling of these levels need to take this data characteristic into account. If groups of patients with different developments of the levels over time are suspected the model class of finite mixtures of mixed effects models with censored data is required.We describe the model specification and derive the estimation with a suitable expectation-maximization algorithm.We propose a convenient implementation using closed form formulae for the expected mean and variance of the truncated multivariate distribution. Only efficient evaluation of the cumulative multivariate normal distribution function is required. Model selection as well as methods for inference are discussed. The application is demonstrated on the clinical trial ACTG 315 data.

Mixed-effects models are commonly used to model longitudinal data as they can appropriately account for within and between subject sources of variability. Univariate mixed effect modeling strategies are well developed for a single outcome (response) variable that may be continuous (e.g. Gaussian) or categorical (e.g. binary, Poisson) in nature. Only recently have extensions been discussed for jointly modeling multiple outcome variables measures longitudinally. Many diseases processes are a function of several factors that are correlated. For example, the metabolic syndrome, a constellation of cardiovascular risk factors associated with an increased risk of cardiovascular disease and type 2 diabetes, is often defined as having three of the following: elevated blood pressure, high waist circumference, elevated glucose, elevated triglycerides, and decreased HDL. Clearly these multiple measures within a subject are not independent. A model that could jointly model two or more of these risk factors and appropriately account for between subjects sources of variability as well as within subject sources of variability due to the longitudinal and multivariate nature of the data would be more useful than several univariate models. In fact, the univariate mixed-effects model can be extended in a relatively straightforward fashion to define a multivariate mixed-effects model for longitudinal data by appropriately defining the variance-covariance structure for the random-effects. Existing software such as the PROC MIXED in SAS can be used to fit the multivariate mixed-effects model. The Fels Longitudinal Study data were used to illustrate both univariate and multivariate mixed-effects modeling strategies. Specifically, jointly modeled longitudinal measures of systolic (SBP) and diastolic (DBP) blood pressure during childhood (ages two to eighteen) were compared between participants who were diagnosed with at least three of the metabolic syndrome risk factors in adulthood (ages thirty to fifty-five) and those who were never diagnosed with any risk factors. By identifying differences in risk factors, such as blood pressure, early in childhood between those who go on to develop the metabolic syndrome in adulthood and those who do not, earlier interventions could be used to prevent the development cardiovascular disease and type 2 diabetes. As demonstrated by these analyses, the multivariate model is able to not only answer the same questions addressed as the univariate model, it is also able to answer additional important questions about the association in the evolutions of the responses as well as the evolution of the associations. Furthermore, the additional information gained by incorporating information about the correlations between the responses was able to reduce the variability (standard errors) in both the fixed-effects estimates (e.g. differences in groups, effects of covariates) as well as the random-effects estimates (e.g. variability).

Este trabalho tem como objetivo aplicar a teoria de modelos mistos ao estudo do teor de nitrogênio e carbono no solo, em diversas profundidades. Devido a grande quantidade de matéria orgânica no solo, o teor de nitrogênio e carbono apresentam alta variabilidade nas primeiras profundidades, além de apresentar um comportamento não linear. Assim, fez-se necessário utilizar a abordagem de modelos não lineares mistos a dados longitudinais. A utilização desta abordagem proporciona um modelo que permite modelar dados não lineares, com heterogeneidade de variâncias, fornecendo uma curva para cada amostra. / This paper has as an objective to apply the theory of mixed models to the content of nitrogen and carbon in the soil at various depths. Due to the large amount of organic material in the soil, the content of nitrogen and carbon present high variability in the depths of soil surface, and present a nonlinear behavior. Thus, it was necessary to use the approach of nonlinear mixed models to longitudinal data analysis. The use of this approach provides a model that allows to model nonlinear data with heterogeneity of variances by providing a curve for each sample.

Longitudinal growth patterns are routinely seen in medical studies where developments of individuals on one or more outcome variables are followed over a period of time. Many current methods for modeling growth presuppose a parametric relationship between the outcome and time (e.g., linear, quadratic); however, these relationships may not accurately capture growth over time. Functional mixed effects (FME) models provide flexibility in handling longitudinal data with nonparametric temporal trends because they allow the data to determine the shape of the curve. Although FME methods are well-developed for continuous, normally distributed outcome measures, nonparametric methods for handling categorical outcomes are limited. In this thesis, we propose a Bayesian hierarchical FME model to account for growth curves with non-Gaussian outcomes. In particular, we extend traditional FME models which assume normally distributed outcomes by modeling the probabilities associated with the binomially distributed outcomes and adding an additional level to the hierarchical model to correctly specify the outcomes as binomially distributed. We then extend the proposed binomial FME model to the multinomial setting where the outcomes consist of more than two nominal categories. Current modeling approaches include modeling each category of a multinomial outcome separately via linear and nonlinear mixed effects models; yet, these approaches ignore the inherent correlation among the categories of the outcome. Our model captures this correlation through a sequence of conditional binomial FME models which results in one model simultaneously estimating probabilities in all categories. Lastly, we extend our binomial FME model to address a common medical situation where multiple outcomes are measured on subjects over time and investigators are interested in simultaneously assessing the impact of all outcomes. We account for the relationship between outcomes by altering the correlation structure in the hierarchical model and simultaneously estimating the outcome curves. Our methods are assessed via simulation studies and real data analyses where we investigate the ability of the models to accurately predict the underlying growth trajectory of individuals and populations. Our applications include analyses of speech development data in adults and children with cochlear implants and analyses on eye-tracking data used to assess word processing in cochlear implant patients.

This study investigates the tool wear of advanced PVD TiALN/TiN multilayer coated end mill inserts when dry and semi-dry machining 4340 low alloy medium carbon steel. A factorial design of experiment setup consisting of two levels of speed, three levels of feed, two levels of depth of cut, and two levels of cutting conditions (semi-dry and dry) was used for the study. The combination of cutting conditions that gave the best response for different components of cutting force, cutting power, surface roughness and tool life were determined using MANOVA & ANOVA analysis and Tukey comparison of means test using MINITAB statistical software package. From a study of the Energy Dispersive X ray (EDX) analysis and primary back scatter images obtained from the worn out crater surface of the insert, it was observed that diffusion wear prevailed under both dry and semi-dry machining conditions. A tool life model was developed using multiple regression analysis within the range of cutting conditions selected. A model for flank wear progression was also developed using mixed effects modeling technique using S Plus statistical software package. This technique takes into account between and within work piece variations during end milling and produces a very accurate model for tool wear progression. This is the first time application of the mixed effects modeling technique in metal cutting literature.