abstract: Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

In June 2008 one of the biggest and most popular sports tournaments took place in Austria and Switzerland, the European football championship 2008 (UEFA EURO 2008). Before the tournament started millions of football supporters throughout the world were asking themselves, just as we did: "Who is going to win the EURO 2008?". We investigate a method for forecasting the tournament outcome, that is not based on historical data (such as scores in previous matches) but on quoted winning odds for each of the 16 teams as provided by 45 international bookmakers. By using a mixed-effects model with a team-specific random effect and fixed effects for the bookmaker and the preliminary group we model the unknown "true" log-odds for winning the championship. The final of the EURO 2008 was played by the teams Germany and Spain. This was exactly the fixture that our method forecasted with a probability of about 20.2%. Furthermore, estimated winning probabilities can be derived from our model, where team Germany, the runner-up of the final had the highest probability (17.6%) to win the title and team Spain the winner of the tournament had the second best chance to win the championship (12.3%). To adjust for effects of the tournament schedule including the group draw, we recovered the latent team strength (underlying the bookmakers' expectations) to answer the question: Will the "best" team win? An ex post analysis of the tournament showed that our method yields good predictions of the tournament outcome and outperforms the FIFA/Coca Cola World rating and the Elo rating. / Series: Research Report Series / Department of Statistics and Mathematics

Genome structure can be classified into three categories: primary structure, secondary structure and tertiary structure, and they are all important for gene transcription regulation. In this research, we utilize the structural information to characterize the correlations and interactions among genes, and involve such information into the Linear Mixed-Effects (LME) model to improve the accuracy of gene expression prediction. In particular, we use chromatin features as predictors and each gene is an observation. Before model training and testing, genes are grouped according to the genome structural information. We use four gene grouping methods: 1) grouping genes according to sliding windows on primary structure; 2) grouping anchor genes in chromatin loop structure; 3) grouping genes in the CTCF-anchored domain; and 4) grouping genes in the chromatin domains obtained from Hi-C experiments. We compare the prediction accuracy between LME model and linear regression model. If all chromatin feature predictors are included into the models, based on the primary structure only (Method 1), the LME models improve prediction accuracy by up to 1%. Based on the tertiary structure only (Methods 2-4), for the genes that can be grouped according the tertiary interaction data, LME models improve prediction accuracy by up to 2.1%. For individual chromatin feature predictors, the LME models improve from 2% to 26 %, in which improvement is more significant for chromatin features that have lower original predictive ability. For future research we propose a model that combines the primary and tertiary structure to infer the correlations among genes to further improve the prediction.

The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

A Linear mixed-effects model (LME) is one of the possible tools for longitudinal or group--dependent data. This thesis deals with evaluating of prediction error in LME. Firstly, it is derived the mean square error of prediction (MSEP) by direct calculation. Then the covariance penalty method and crossvalidation is presented for evaluation of MSEP in LME. Further, it is shown how Akaike information criterion (AIC) can be used in mixed-effects models. Because of the model's properties two types of AIC are distinguished - marginal and conditional one. Subsequently, the procedures of AIC's calculation and its basic asymptotic properties are described. Finally, the thesis contains simulation study of behaviour of marginal and conditional AIC with the goal to choose the right variance structure of random effects. It turns out that the marginal criterion tends to select models with smaller number of random effects than conditional criterion.

This dissertation focuses on estimation and selection methods for a simple linear model with two levels of variation. This model provides a foundation for extensions to more levels. We propose new regularization criteria for model selection, subset selection, and variable selection in this context. Regularization is a penalized-estimation approach that shrinks the estimate and selects variables for structured data. This dissertation introduces a procedure (HM-ALASSO) that extends regularized multilevel-model estimation and selection to enforce principles of fixed heredity (e.g., including main effects when their interactions are included) and random heredity (e.g., including fixed effects when their random terms are included). The goals in developing this method were to create a procedure that provided reasonable estimates of all parameters, adhered to fixed and random heredity principles, resulted in a parsimonious model, was theoretically justifiable, and was able to be implemented and used in available software. The HM-ALASSO incorporates heredity-constrained selection directly into the estimation process. HM-ALASSO is shown to enjoy the properties of consistency, sparsity, and asymptotic normality. The ability of HM-ALASSO to produce quality estimates of the underlying parameters while adhering to heredity principles is demonstrated using simulated data. The performance of HM-ALASSO is illustrated using a subset of the High School and Beyond (HS&B) data set that includes math-achievement outcomes modeled via student- and school-level predictors. The HM-ALASSO framework is flexible enough that it can be adapted for various rule sets and parameterizations. / Statistics

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.

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.

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.

The question of adverbs and the meaning of the present perfect across varieties of English is central to sociolinguistic variationist methodologies that have approached the study of the present perfect (Winford, 1993; Tagliamonte, 1997; van Herk, 2008, 2010; Davydova, 2010; Tagliamonte, 2013). This dissertation attempts to disentangle the effect of adverbial support from the three canonical readings of the present perfect (Resultative, Experiential and Continuative). Canadian English, an understudied variety of English, is used to situate the results seen in the Early Modern English data. Early Modern English reflects the time period in which English has acquired the full modern use of the present perfect with the three readings. In order to address both these questions and current controversies over statistical models in sociolinguistics, different statistical models are used: both the traditional Goldvarb X (Sankoff, Tagliamonte and Smith, 2005) and the newer mixed-effects logistic regression (Johnson, 2009). What is missing from the previous literature in sociolinguistics that advocates logistic mixed-effects models, and provided in this dissertation, is a clear statement of where they are inappropriate to use and their limitations. The rate of adverbial marking of the present perfect in Canadian English falls between rates reported for US and British English in previous studies. The data show in both time periods that while adverbs are highly favored in continuative contexts, they are strongly disfavored in experiential and resultative contexts. In Early Modern English, adverbial support functions statistically differently for resultatives and experientials, but that difference collapses in the Canadian English sample. Both this and the other linguistic contexts support a different analysis for each set of data with respect to adverbial independence from the meaning of the present perfect form. Finally, when the focus of the analysis is on linguistic rather than social factors, both the traditional and newer models provide similar results. Where there are differences, however, these can be accounted for by the number of tokens and different estimation techniques for each model.