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This dissertation aims to develop methods for statistical predictions based on various types of data from different areas. We focus on applications from reliability and spatial epidemiology. Chapter 1 gives a general introduction of statistical predictions. Chapters 2 and 3 investigate the photodegradation of an organic coating, which is mainly caused by ultraviolet (UV) radiation but also affected by environmental factors, including temperature and humidity. In Chapter 2, we identify a physically motivated nonlinear mixed-effects model, including the effects of environmental variables, to describe the degradation path. Unit-to-unit variabilities are modeled as random effects. The maximum likelihood approach is used to estimate parameters based on the accelerated test data from laboratory. The developed model is then extended to allow for time-varying covariates and is used to predict outdoor degradation where the explanatory variables are time-varying. Chapter 3 introduces a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with random effects to describe the degradation paths and a vector time series model to describe the covariate process. Shape restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters of these models are estimated by using the maximum likelihood method. Algorithms for computing the estimated lifetime distribution are also described. The proposed methods are applied to predict the photodegradation path of an organic coating in a complicated dynamic environment. Chapter 4 investigates the Lyme disease emergency in Virginia at census tract level. Based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011, we analyze the spatial patterns of the disease using statistical smoothing techniques. We also use the space and space-time scan statistics to reveal the presence of clusters in the spatial and spatial/temporal distribution of Lyme disease. Chapter 5 builds a predictive model for Lyme disease based on historical data and environmental/demographical information of each census tract. We propose a Divide-Recombine method to take advantage of parallel computing. We compare prediction results through simulation studies, which show our method can provide comparable fitting and predicting accuracy but can achieve much more computational efficiency. We also apply the proposed method to analyze Virginia Lyme disease spatio-temporal data. Our method makes large-scale spatio-temporal predictions possible. Chapter 6 gives a general review on the contributions of this dissertation, and discusses directions for future research. / Ph. D.

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This is a study on the effect of management and CEO stock ownership on company performance. A regression analysis is performed on panel data consisting of a sample of 30 companies listed on OMX Stockholm Mid Cap. A total of 210 and 2520 observations is considered on a yearly and monthly basis, respectively, for seven years (2010-2016). The Hausman test is applied for determining between the fixed effects and random effects regression models. Results show that management relative stock ownership has a significant positive effect on company net income growth and return on assets. The effect is not significant for CEO stock ownership, which is contrary to what commonly has been shown for large companies in previous research. Moreover, alternative methodology is discussed for the benefit of the future researcher. The authors illustrate how the selection of dummy variables can be vital for final model outcomes, and it is thus an important aspect to consider when performing panel data analysis. / I den här studien undersöks hur aktieinnehav hos ledning och den verkställande direktören i ett företag påverkar företagsutvecklingen. Studien genomförs med regressionsanalys på paneldata som består av 30 företag, samtliga noterade på OMX Stockholm Mid Cap. Totalt samlas 210 och 2520 observationer på årsbasis respektive månadsbasis över sju år (2010-2016). Hausman-testet används för att bestämma vilken av fixed effects-modellen och random effects-modellen som ska användas i regressionen. Resultaten visar att relativt aktieinnehav hos ledningen har en positiv signifikant påverkan på ett företags nettoinkomstutveckling och avkastning på tillgångar. Den verkställande direktörens aktieinnehav visas inte vara signifikant, vilket är motsatt till det som generellt har visats för stora företag i tidigare forskning. Regressionerna genomförs även med alternativa metoder, vilka det resoneras kring i en diskussion som bör vara till gagn för vidare forskning. Författarna illustrerar hur val av dummy-variabler kan ha en avgörande betydelse för regressionsanalysen, och att det således är en viktig aspekt att ta hänsyn till när regressioner genomförs på paneldata.

Os modelos de mistura são muito eficazes para analisar dados compostos por diferentes subpopulações com alocações desconhecidas ou que apresentam assimetria, multimodalidade ou curtose. Esta tese propõe relacionar a distribuição de probabilidade beta e a técnica de ajuste de modelos mistos à metodologia de modelos de mistura para que sejam adequados na análise de dados que assumem valores em um intervalo restrito conhecido e que também são caracterizados por possuírem uma estrutura de agrupamento ou hierárquica. Foram especificados os modelos de mistura beta mistos linear, com dispersão constante e variável, e não linear. Foi considerada uma abordagem bayesiana com uso de métodos de Monte Carlo via Cadeias de Markov (MCMC). Estudos de simulação foram delineados para avaliar os resultados inferenciais destes modelos em relação à acurácia da estimação pontual dos parâmetros, ao desempenho de critérios de informação na seleção do número de elementos da mistura e ao diagnóstico de identificabilidade obtido com o algoritmo data cloning. O desempenho dos modelos foi muito promissor, principalmente pela boa acurácia da estimação pontual dos parâmetros e por não haver evidências de falta de identificabilidade. Três bancos de dados reais das áreas de saúde, marketing e educação foram estudados por meio das técnicas propostas. Tanto nos estudos de simulação quanto na aplicação a dados reais se obtiveram resultados muito satisfatórios que evidenciam tanto a utilidade dos modelos desenvolvidos aos objetivos tratados quanto a potencialidade de aplicação. Ressaltando que a metodologia apresentada também pode ser aplicada e estendida a outros modelos de mistura. / Mixture models are very effective for analyzing data composed of different subpopulations with unknown allocations or with asymmetry, multimodality or kurtosis. This work proposes to link the beta probability distribution and the mixed models to the methodology of mixture models so that they are suitable to analyse data with values in a restricted and known interval and that also are characterized by having a grouping or hierarchical structure. There were specified the linear beta mixture models with random effects, with constant and varying dispersion, and also the nonlinear one with constant dispersion. It was considered a Bayesian approach using Markov Chain Monte Carlo (MCMC) methods. Simulation studies were designed to evaluate the inferential results of these models in relation to the accuracy of the parameter estimation, to the performance of information criteria in the selection of the number of elements of the mixture and to the diagnosis of identifiability obtained with the algorithm data cloning. The performance of the models was very promising, mainly due to the good accuracy of the point estimation of the parameters and because there was no evidence of lack of identifiability of the model. Three real databases of health, marketing and education were studied using the proposed techniques. In both the simulation studies and the application to real data had very satisfactory results that show both the usefulness of the models developed to the treated objectives and the potentiality of application. Note that the presented methodology can also be applied and extended to other mixing models.

Les politiques environnementales, notamment celles recourant aux instruments fiscaux, ont pris une place de plus en plus importante dans un grand nombre de pays durant les trois dernières décennies. Tous les pays de l’OCDE ont introduit des taxes liées à l'environnement et un nombre croissant d'entre eux procèdent à une réforme dite "verte" de leur fiscalité. L’utilisation de la taxe comme un instrument pour la politique environnementale a suscité un large débat parmi les chercheurs sur ses impacts sur la croissance économique, mais sans parvenir à un consensus sur la nature de ces effets. Certains trouvent un effet négatif, alors que d’autres montrent un impact positif. Deux points ont attiré notre attention sur ce sujet. Premièrement, les études empiriques qui vérifient la validité de ces résultats sont très rares. Deuxièmement, la majorité des modèles théoriques qui ont étudié l’effet de la fiscalité environnementale sur la croissance économique supposent que le gouvernement finance ses dépenses uniquement par les taxes et que le budget d’État est équilibré à chaque période, évitant ainsi tout fardeau associé au remboursement de la dette publique. Par conséquent, cette thèse a pour objectif d’abord d’explorer empiriquement la nature de la relation entre la fiscalité environnementale et la croissance économique, et si cette relation est sensible au niveau d'autres variables dans l'économie. Ensuite, nous examinons les canaux par lesquels cette taxe peut affecter la croissance économique, et si l'existence et le niveau de la dette publique peuvent modifier cet effet. / Environmental policies, especially those using fiscal instruments, have become more and more important in a large number of countries over the last three decades. All OECD countries have introduced environmentally related taxes, and a growing number of them are carrying out a so-called "green" reform of their taxation. The use of the tax as an instrument for environmental policy has sparked wide debate among researchers on its impacts on economic growth, but without reaching consensus on the nature of these effects. Some find a negative effect; while others show a positive impact. Two points raised our attention on this subject. First, the empirical studies that verify the validity of these results are very rare. Second, the majority of theoretical models that have studied the effect of environmental taxation on economic growth assume that the government finances its expenditures solely through taxes and that the state budget is balanced each period, thus avoiding any burden associated to repayment of public debt. Therefore, this thesis aims firstly to explore empirically the nature of the relationship between environmental taxation and economic growth, and whether this relationship is sensitive to the level of other variables in the economy. We examine then the channels through which this tax can affect economic growth, and whether the existence and level of public debt can modify this effect.

The United States cheese consumption has grown considerably over the years. Using Nielsen Homescan panel data for calendar years 2005 and 2006, this dissertation examines the effect of economic and socio-demographic factors on the demand for disaggregated cheese varieties and on the cheese industry in general. In the first essay, we estimated the censored demand for 14 cheese varieties and identified the respective own-price and cross-price elasticities. Also, non-price factors were determined affecting the purchase of each variety as well as the impact of generic dairy advertising. Results revealed that most of the natural cheese varieties have an elastic demand while the processed cheese products exhibited inelastic demands. Strong substitution and complementarity relationships were identified as well, and a two quarter carry-over effect of advertising was observed for most of cheese demands. Results also showed that household demographics affected the demands differently, depending on the nature of the cheese varieties. The second essay examined the impact of retail promotion on the decision to purchase private label processed cheese products using a probit model. A strong negative relationship was found between national brand manufacturer couponing activity and the private label purchase decision. Therefore, national brand couponing appears to be an effective strategy for manufacturers to deter private label growth. This analysis also shows that the decision of purchasing a private label cheese product is influenced by socio-demographic characteristics of the household, namely household income and size, age and education level of the household head, race, ethnicity, and location. In the third study, the feasibility of fortifying processed cheese with omega-3 is investigated. This ex-ante analysis took into account the market conditions and evaluates the increase in the demand for processed cheese needed to offset the costs of fortification in order to maintain the profitability of manufacturers like Kraft. Initially, the censored demand for processed cheese products is estimated using panel data; subsequently, the profitability of manufacturing such product is determined.This analysis shows that, within reasonable market conditions and reasonable marginal costs, the fortification of processed cheese products with omega-3 fatty acids indeed is feasible from a profitability standpoint to manufacturers.

This dissertation analyses the effects of current fisheries practices and management regulations of the green spiny lobster (Panulirus gracilis) in the Eastern Tropical Pacific Region (ETP). P. gracilis has reached a critical state in the ETP. Country-based studies report that between 60 and 98% of lobsters caught in the wild are under the minimum landing size (MLS). This means that spiny lobsters are being extracted before reproducing and contributing to the replenishment of interconnected populations. The recovery of green spiny lobster populations in the ETP and the future maintenance of a sustainable fishery will depend on effective management decisions and on taking in account environmental factors that influence the population dynamics of the lobsters. In the first study (Chapter 2), the B52 Spiny Lobster individual based simulation model was used for conducting a population viability analysis to quantify the effect of current fishing practices and the effect of varying management regulations on minimum landing size (MLS) and fishing effort. The best suit of regulations to maintain the highest abundance, production of offspring and catch is to protect juveniles and egged females, and to establish a MLS that assures the reproduction of individuals before being extracted. This study revealed regional variations, however the patterns and the causes for variation were not yet clear. This led to the next chapters in this dissertation. In Chapter 3, I used a meta-analysis to explore regional lobster variability by comparing published studies from the ETP. The objective was to identify patterns of variation related to geographic and environmental factors of the region that can inform the establishment and evaluation of coordinated regulations. Morphological relationships showed to be more variable at northern latitudes, where the mean annual sea surface temperatures are higher than at lower temperatures at the Equator. In terms of management, MLS regulations should be adapted accounting for the effect of sea surface temperature and its variation. Additionally, it was observed that monitoring methodologies are not standardized within the region and even in some cases, neither within countries. Furthermore, in most places monitoring of the spiny lobster fishery happens sporadically, only in Galapagos takes place every year. Identifying patterns of variations can improve the accuracy of prediction models which can help to explore, design, and apply more effective management measures, as well as promote regional coordination to support the recovery and maintenance of spiny lobsters. In Chapter 4, I contrast current Ecuadorian minimum landing size (MLS) regulation to lobster empirical measurements within Ecuador in order to recognize potential pitfalls for management enforcement. I used linear regression and multiple regression models with the objective of identifying potential relative size variations of the individuals caught in the wild over time and in the different fishing areas in Ecuador, as well as to analyse the effect of locality, sex, age and mean SST on the tail length/total length ratio. Morphological relations were significantly different among sexes, in time and by all sites. Most importantly, this study shows that current minimum size regulations are not applicable to all sites. Additionally, I found that water temperature has a significant effect on morphological relationship variations. However, it was not the main site-specific variable responsible for explaining such variations. In general, this work emphasizes the need for length data collection standardization and the consideration of temporal and spatial variation implications in national and regional fishery management planning, enforcement and evaluation.