Intensive selection for increased milk production over many generations has led to growing genetic similarity and increased relationships in dairy population. In the current study, inbreeding depression was estimated for number of days to first service, summit milk, conception by 70 days non-return, and calving rate with a linear mixed model (LMM) approach and for calving difficulty, calf mortality with a Bayesian threshold model (BTM) for categorical traits. Effectiveness of classical and unknown parentage group procedures to estimate inbreeding coefficients was evaluated depending on completeness of a 5-generation pedigree. A novel method derived from the classical formula to estimate inbreeding was utilized to evaluate completeness of pedigrees. Two different estimates of maternal inbreeding were fitted in separate models as a linear covariate in combined LMM analyses (Holstein registered and grade cows and Jersey cows) or separate analyses (registered Holstein cows) by parity (1-4) with fetal inbreeding. Impact of inbreeding type, model, data structure, and treatment of herd-year-season (HYS) on magnitude and size of inbreeding depression were assessed. Grade Holstein datasets were sampled and analyzed by percentage of pedigree present (0-30%, 30-70% and 70-100%). BTM analyses (sire-mgs) were performed using Gibbs sampling for parities 1, 2 and 3 fitting maternal inbreeding only. In LMM analyses of grade data, the least pedigree and diagonal A matrix performed the worst. Significant inbreeding effects were obtained in most traits in cows of parity 1. Fetal inbreeding depression was mostly lower than that from maternal inbreeding. Inbreeding depression in binary traits was the most difficult to evaluate. Analyses with non-additive effects included in LMM, for data by inbreeding level and by age group should be preferred to estimate inbreeding depression. In BTM inbreeding effects were strongly related to dam parity and calf sex. Largest effects were obtained from parity 1 cows giving birth to male calves (0.417% and 0.252% for dystocia and calf mortality) and then births to female calves (0.300% and 0.203% for dystocia and calf mortality). Female calves from mature cows were the least affected (0.131% and 0.005% for dystocia and calf mortality). Data structure was found to be a very important factor to attainment of convergence in distribution. / Ph. D.

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The initial portion of this research focuses on appropriate parameter estimators within a general context of multivariate quality control. The goal of Phase I analysis of multivariate quality control data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. Because of the inherent difficulties in computation many algorithms have been proposed to obtain them. We consider the subsampling algorithm to obtain the MVE estimators and the FAST-MCD algorithm to obtain the MCD estimators. Previous studies have not clearly determined which of these two estimation methods is best for control chart applications. The comprehensive simulation study here gives guidance for when to use which estimator. Control limits are provided. High breakdown estimation methods such as MCD and MVE can be applied to a wide variety of multivariate quality control data. The final, lengthier portion of this research considers profile monitoring. Profile monitoring is a relatively new technique in quality control used when the product or process quality is best represented by a profile (or a curve) at each time period. The essential idea is often to model the profile via some parametric method and then monitor the estimated parameters over time to determine if there have been changes in the profiles. Because the estimated parameters may be correlated, it is convenient to monitor them using a multivariate control method such as the T-squared statistic. Previous modeling methods have not incorporated the correlation structure within the profiles. We propose the use of mixed models (both linear and nonlinear) to monitor linear and nonlinear profiles in order to account for the correlation structure within a profile. We consider various data scenarios and show using simulation when the mixed model approach is preferable to an approach that ignores the correlation structure. Our focus is on Phase I control chart applications. / Ph. D.

The goal of this thesis is to explore, improve and implement some advanced modern computational methods in statistics, focusing on applications in genetics. The thesis has three major directions. First, we study likelihoods for genetics analysis of experimental populations. Here, the maximum likelihood can be viewed as a computational global optimization problem. We introduce a faster optimization algorithm called PruneDIRECT, and explain how it can be parallelized for permutation testing using the Map-Reduce framework. We have implemented PruneDIRECT as an open source R package, and also Software as a Service for cloud infrastructures (QTLaaS). The second part of the thesis focusses on using sparse matrix methods for solving linear mixed models with large correlation matrices. For populations with known pedigrees, we show that the inverse of covariance matrix is sparse. We describe how to use this sparsity to develop a new method to maximize the likelihood and calculate the variance components. In the final part of the thesis we study computational challenges of psychiatric genetics, using only pedigree information. The aim is to investigate existence of maternal effects in obsessive compulsive behavior. We add the maternal effects to the linear mixed model, used in the second part of this thesis, and we describe the computational challenges of working with binary traits. / eSSENCE

Displays are ubiquitous throughout the animal kingdom. While many have been thoroughly documented, the factors affecting the expression of such displays are still not fully understood. We tested the hypotheses that display production would be affected by ecological context (i.e. the identity of the receiver) and intrinsic qualities of the signaler (i.e. heavyweight and lightweight size class) in the green anole lizard, Anolis carolinensis. Our results supported these predictions and show that a) ecological context, specifically displaying to conspecifics, has the greatest impact on display production; b) size class influenced display rate with heavyweight males displaying more to green females and lightweight males displaying more to green males in similar frequency between the two size classes to their respective target stimuli. Furthermore, our results provide empirical support for differential use of the three major display types (A, B and C displays), and uncover unexpected complexity in green anole display production.

Longitudinal data, which is also known as repeated measures, has grown increasingly within the past years because of its ability to monitor change both within and between subjects. Statisticians in many fields of study have chosen this way of collecting data because it is cost effective and it minimizes the number of subjects required to produce a meaningful outcome. This thesis will explore the world of longitudinal studies to gain a thorough understanding of why this type of collecting data has grown so rapidly. This study will also describe several methods to analyze repeated measures using data collected on the behavior of both adolescent and adult rats. The question of interest is to see if there is a change in the mean response over time and if the covariates (age, bodyweight, gender, and time) influence those changes. After much testing, our data set has a positive nonlinear change in the mean response over time within the age and gender groups. Using a model that included random effects proved to be a better method than models that did not use any random effects. Taking the log of the response variable and using day as the random effect was overall a better fit for our dataset. The transformed model also showed all covariates except for age as being significant.

abstract: This paper investigates a relatively new analysis method for longitudinal data in the framework of functional data analysis. This approach treats longitudinal data as so-called sparse functional data. The first section of the paper introduces functional data and the general ideas of functional data analysis. The second section discusses the analysis of longitudinal data in the context of functional data analysis, while considering the unique characteristics of longitudinal data such, in particular sparseness and missing data. The third section introduces functional mixed-effects models that can handle these unique characteristics of sparseness and missingness. The next section discusses a preliminary simulation study conducted to examine the performance of a functional mixed-effects model under various conditions. An extended simulation study was carried out to evaluate the estimation accuracy of a functional mixed-effects model. Specifically, the accuracy of the estimated trajectories was examined under various conditions including different types of missing data and varying levels of sparseness. / Dissertation/Thesis / Masters Thesis Psychology 2016

Resumo: Objetivo deste trabalho foi a identificação das formas de competição em testes de progênies de eucalipto e a influencia desta nos parâmetros genéticos e na seleção. Foram usados dados de dois testes de progênies de polinização aberta de eucalipto, instalados no delineamento em blocos casualizados. O experimento 1 (EXP1) foi constituído por quatro testemunhas (clones) e 49 progênies e o experimento 2 (EXP2) por 44 progênies, ambos com seis repetições e parcelas lineares de 10 plantas. Em três idades (aos 2, 4 e 7 anos para o EXP1 e aos 3, 5 e 7 anos para o EXP2), avaliou-se o crescimento em altura (ALT), o diâmetro à altura do peito (DAP) e o volume comercial de madeira com casca (VOL), analisando-se os dados pela metodologia REML/BLUP. Para VOL aos sete anos de idade foi feita a análise de covariância para identificação da competição e suas formas através de sete covariáveis: índice de competição de Hegyi (IC), auto-competição (AT), alo-competição (AL), média da autocompetição (MAT), média da alocompetição (MAL) e media aritmética dos quatro (M4) e oito vizinhos mais próximos (M8). O efeito dessas covariáveis foi estudado individualmente e em todas as suas possíveis combinações, avaliando-se as alterações em todos os componentes de variância. Ainda aos sete anos foi efetuada a seleção para os três caracteres, com e sem o auxílio de covariáveis de competição e, para VOL, em todas as idades, foi simulada a seleção com e sem o auxilio de covariáveis de competição, comparando-se os parâmetros genéticos e a eficiência da seleção precoce em relação à idade adulta. A auto-competição parece causar menor variabilidade genética e erro entre parcelas, já a alo-competição interfere de forma oposta. A rotina de análise que apresentou melhores resultados foi a que incluiu as covariáveis MAT/IC. A competição intergenotípica causa... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The aim in this study was to identify the forms of competition in eucalyptus progeny tests and their influence on genetic parameters and selection. Data from two progeny tests of open pollinated families of eucalyptus were used. The plants were set up in a randomized plot design, in which experiment 1 (EXP1) comprised four control (clones) and 49 families and experiment 2 (EXP2) comprised 44 families. Both experiments consisted of six replications and linear plots of 10 plants. At three ages (2, 4 and 7 years old for EXP1 and 3, 5 and 7 years old for EXP2), growth was assessed in terms of height (H), diameter at breast height (DBH) and commercial volume of wood with bark (VOL). The data were analyzed using the REML/BLUP methodology. For VOL at seven years old, covariance analysis was performed to identify the competition and its forms, through seven covariables: Hegyi competition index (HCI), autocompetition (AUT), allocompetition (AL), mean autocompetition (MAT), mean allocompetition (MAL) and arithmetic mean of the closest four (M4) and eight (M8) neighbors. The effect from these covariables was studied singly and in all possible combinations, and the changes to all variance components were evaluated. Selection for three characteristics with or without the aid of competition covariables was also done after seven years old. For VOL, at all ages, selection with or without competition covariables was simulated by comparing the genetic parameters and early selection efficiency in relation to mature age. Autocompetition seemed to cause less genetic variability and error between plots, while allocompetition had the opposite effect. The analysis routine that presented the best results was the one that included the MAT and HCI covariables. Intergenotype competition caused selection bias among the eucalyptus open pollinated families and might have given rise to incorrect choice of genetic... (Complete abstract click electronic access below) / Orientador: Rinaldo César de Paula / Coorientador: Dilermando Perecin / Banca: Miguel Luiz Menezes Freitas / Banca: Alexandre Magno Sebbenn / Banca: Mario Luiz Teixeira de Moraes / Banca: José Roberto Moro / Doutor

Power system planning, reliability analysis and economically efficient capacity scheduling all rely heavily on electricity demand forecasting models. In the context of a deregulated wholesale electricity market, using scheduling a region’s bulk electricity generation is inherently linked to future values of demand. Predictive models are used by municipalities and suppliers to bid into the day-ahead market and by utilities in order to arrange contractual interchanges among neighboring utilities. These numerical predictions are therefore pervasive in the energy industry. This research seeks to develop a regression-based forecasting model. Specifically, electricity demand is modeled as a function of calendar effects, lagged demand effects, weather effects, and a stochastic disturbance. Variables such as temperature, wind speed, cloud cover and humidity are known to be among the strongest predictors of electricity demand and as such are used as model inputs. It is well known, however, that the relationship between demand and weather can be highly nonlinear. Rather than assuming a linear functional form, the structural change in these relationships is explored. Those variables that indicate a nonlinear relationship with demand are accommodated with penalized splines in a semiparametric regression framework. The equivalence between penalized splines and the special case of a mixed model formulation allows for model estimation with currently available statistical packages such as R, STATA and SAS. Historical data are available for the entire New England region as well as for the smaller zones that collectively make up the regional grid. As such, a secondary research objective of this thesis is to explore whether or not an aggregation of zonal forecasts might perform better than those produced from a single regional model. Prior to this research, neither the applicability of a semiparametric regression-based approach towards load forecasting nor the potential improvement in forecasting performance resulting from zonal load forecasting has been investigated for the New England wholesale electricity market.

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