<p>Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the&nbsp / development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do.&nbsp / There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.</p>

Binary measurement systems (BMS) are widely used in both manufacturing industry and medicine. In industry, a BMS is often used to measure various characteristics of parts and then classify them as pass or fail, according to some quality standards. Good measurement systems are essential both for problem solving (i.e., reducing the rate of defectives) and to protect customers from receiving defective products. As a result, it is desirable to assess the performance of the BMS as well as to separate the effects of the measurement system and the production process on the observed classifications. In medicine, BMSs are known as diagnostic or screening tests, and are used to detect a target condition in subjects, thus classifying them as positive or negative. Assessing the performance of a medical test is essential in quantifying the costs due to misclassification of patients, and in the future prevention of these errors. In both industry and medicine, the most commonly used characteristics to quantify the performance a BMS are the two misclassification rates, defined as the chance of passing a nonconforming (non-diseased) unit, called the consumer's risk (false positive), and the chance of failing a conforming (diseased) unit, called the producer's risk (false negative). In most assessment studies, it is also of interest to estimate the conforming (prevalence) rate, i.e. probability that a randomly selected unit is conforming (diseased). There are two main approaches for assessing the performance of a BMS. Both approaches involve measuring a number of units one or more times with the BMS. The first one, called the "gold standard" approach, requires the use of a gold-standard measurement system that can determine the state of units with no classification errors. When a gold standard does not exist, is too expensive or time-consuming, another option is to repeatedly measure units with the BMS, and then use a latent class approach to estimate the parameters of interest. In industry, for both approaches, the standard sampling plan involves randomly selecting parts from the population of manufactured parts. In this thesis, we focus on a specific context commonly found in the manufacturing industry. First, the BMS under study is nondestructive. Second, the BMS is used for 100% inspection or any kind of systematic inspection of the production yield. In this context, we are likely to have available a large number of previously passed and failed parts. Furthermore, the inspection system typically tracks the number of parts passed and failed; that is, we often have baseline data about the current pass rate, separate from the assessment study. Finally, we assume that during the time of the evaluation, the process is under statistical control and the BMS is stable. Our main goal is to investigate the effect of using sampling plans that involve random selection of parts from the available populations of previously passed and failed parts, i.e. conditional selection, on the estimation procedure and the main characteristics of the estimators. Also, we demonstrate the value of combining the additional information provided by the baseline data with those collected in the assessment study, in improving the overall estimation procedure. We also examine how the availability of baseline data and using a conditional selection sampling plan affect recommendations on the design of the assessment study. In Chapter 2, we give a summary of the existing estimation methods and sampling plans for a BMS assessment study in both industrial and medical settings, that are relevant in our context. In Chapters 3 and 4, we investigate the assessment of a BMS in the case where we assume that the misclassification rates are common for all conforming/nonconforming parts and that repeated measurements on the same part are independent, conditional on the true state of the part, i.e. conditional independence. We call models using these assumptions fixed-effects models. In Chapter 3, we look at the case where a gold standard is available, whereas in Chapter 4, we investigate the "no gold standard" case. In both cases, we show that using a conditional selection plan, along with the baseline information, substantially improves the accuracy and precision of the estimators, compared to the standard sampling plan. In Chapters 5 and 6, we investigate the case where we allow for possible variation in the misclassification rates within conforming and nonconforming parts, by proposing some new random-effects models. These models relax the fixed-effects model assumptions regarding constant misclassification rates and conditional independence. As in the previous chapters, we focus on investigating the effect of using conditional selection and baseline information on the properties of the estimators, and give study design recommendations based on our findings. In Chapter 7, we discuss other potential applications of the conditional selection plan, where the study data are augmented with the baseline information on the pass rate, especially in the context where there are multiple BMSs under investigation.

Várias propostas têm surgido nos últimos anos para problemas que envolvem a predição de observações futuras em modelos mistos, contudo, para os casos em que o problema trata-se em atribuir valores para os efeitos aleatórios de novos grupos existem poucos trabalhos. Tamura, Giampaoli e Noma (2013) propuseram um método que consiste na computação das distâncias entre o novo grupo e os grupos com efeitos aleatórios conhecidos, baseadas nos valores das covariáveis, denominado Método de Predição do Vizinho Mais Próximo ou NNPM (Nearest Neighbors Prediction Method), na sigla em inglês, considerando o modelo logístico misto. O objetivo deste presente trabalho foi o de estender o método NNPM para o modelo Poisson misto, além da obtenção de intervalos de confiança para as predições, para tais fins, foram propostas novas medidas de desempenho da predição e o uso da metodologia Bootstrap para a criação dos intervalos. O método de predição foi aplicado em dois conjuntos de dados reais e também no âmbito de estudos de simulação, em ambos os casos, obtiveram-se bons desempenhos. Dessa forma, a metodologia NNPM apresentou-se como um método de predição muito satisfatório também no caso Poisson misto. / Many proposals have been created in the last years for problems in the prediction of future observations in mixed models, however, there are few studies for cases that is necessary to assign random effects values for new groups. Tamura, Giampaoli and Noma (2013) proposed a method that computes the distances between a new group and groups with known random effects based on the values of the covariates, named as Nearest Neighbors Prediction Method (NNPM), considering the mixed logistic model. The goal of this dissertation was to extend the NNPM for the mixed Poisson model, in addition to obtaining confidence intervals for predictions. To attain such purposes new prediction performance measures were proposed as well as the use of Bootstrap methodology for the creation of intervals. The prediction method was applied in two sets of real data and in the simulation studies framework. In both cases good performances were obtained. Thus, the NNPM proved to be a viable prediction method also in the mixed Poisson case.

Longitudinal data are common in biological research. However, real data sets vary considerably in terms of their structure and complexity and present many challenges for statistical modelling. This thesis proposes a series of methods using random coefficients for modelling two broad types of longitudinal response: normally distributed measurements and binary recapture data. Biased inference can occur in linear mixed-effects modelling if subjects are drawn from a number of unknown sub-populations, or if the residual covariance is poorly specified. To address some of the shortcomings of previous approaches in terms of model selection and flexibility, this thesis presents methods for: (i) determining the presence of latent grouping structures using a two-step approach, involving regression splines for modelling functional random effects and mixture modelling of the fitted random effects; and (ii) flexible of modelling of the residual covariance matrix using regression splines to specify smooth and potentially non-monotonic variance and correlation functions. Spatially explicit capture-recapture methods for estimating the density of animal populations have shown a rapid increase in popularity over recent years. However, further refinements to existing theory and fitting software are required to apply these methods in many situations. This thesis presents: (i) an analysis of recapture data from an acoustic survey of gibbons using supplementary data in the form of estimated angles to detections, (ii) the development of a multi-occasion likelihood including a model for stochastic availability using a partially observed random effect (interpreted in terms of calling behaviour in the case of gibbons), and (iii) an analysis of recapture data from a population of radio-tagged skates using a conditional likelihood that allows the density of animal activity centres to be modelled as functions of time, space and animal-level covariates.

Magister Scientiae - MSc / Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do. There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections. / South Africa

This thesis analyzes determinants of housing construction in regions in the Czech Republic. The main research question is the impact of standard of living on housing construction. The living standard is expressed in terms of net disposable income per capita and housing construction represents the number of housing starts. Other determinants included to the model estimation are rate of unemployment, housing price and number of mortgage. Analysis works with the panel data from period 2005- 2015 and all variables are used in the logarithmic form with one year lag. The model is estimated by random effects model. The assumption about positive impact of living standard on housing construction is not confirmed, because of the statistical insignificance of variable net disposable income. In case of other variables expected effects are confirm. The increases in rate of unemployment and housing prices have the negative impact on housing construction. And opposite the number of mortgage has positive impact on housing construction.

Introduction: One of the major barriers to developing an accurate tuberculosis (TB) surveillance program for Florida is the design and implementation of a sampling system that will adequately monitor and predict varying sizes and characteristics of county-level vulnerable endemic sub-populations and their explanatory covariates (e.g., living or working in a residential care facility). The aim of this research study is to envision an endemic, tuberculosis-related web-based interface for use by public health officials in the State of Florida which includes generating essential information such as a real-time syndrome-based reporting to regulate automated and immediate 'Alerts' to public health officials, doctors, hospitals and local community in ArcGIS. This study demonstrates the capability of an autocorrelation, time series, epidemiological, interpolative, and vulnerable predictive ArcGIS model to target tuberculosis at the county-level in the state of Florida. Methodology: The data for constructing an autocorrelation, probabilistic paradigm was acquired from the Centers for Disease Control and Prevention [CDC] in Atlanta, Georgia. The full dataset contained two points in time, allowing estimation of a mixed binomial model that aided in predicting the probability of tuberculosis by county. The random effects term in the ArcGIS model was comprised of spatially structured and stochastic effects (i.e., spatially unstructured) terms. These terms substituted for covariates in the model. The assumption was that random effects term in the endemic, TB–related, explanative, county-level, risk model had a frequency distribution that was bell-shaped (i.e., normally/Gaussian distributed) with a mean of zero. Results: The results indicated the empirical estimate had a mean of 0.0197 with a Shapiro-Wilk normality probability of 0.0027. The mean in the model was not exactly zero, although the forecasts indicated 0.06, which was not significantly different from zero. It was noted that the frequency distribution deviated from a bell-shaped curve. This random effects term accounted for roughly 41% of the variability in the observed probability of TB by county and yielded an under dispersed binomial model. An eigenvector spatial filter description of the random effects term involved 5 of 18 total eigenvectors, which portrayed noticeable positive spatial autocorrelation. The decomposition algorithm also revealed 4 of 25 eigenvectors portraying noticeable negative spatial autocorrelation. These two spatial filter components accounted for, respectively, roughly 16% and 10% of the variability in the probability of TB by county. The spatially unstructured random effects component accounted for roughly 15% of this variability. The final model revealed that from 2015 to 2020, Duval, Orange, and Broward counties would require immediate intervention in order to prevent TB transmission. The model also revealed that from 2025 to 2040 Hillsborough and Palm Beach counties could become hyper-endemic without implementation of control strategies. Conclusion: An endemic, TB-related, ArcGIS, autocorrelation eigenanalyses forecast, paradigm may be employed by public health officials in Florida to target, vulnerable, county level ,populations A Precede-Proceed model-based reporting mechanism may help disseminate the ArcGIS model results and help regulate automated and immediate 'Alerts' to public health officials, doctors, hospitals and local community at the county-level. An ArcGIS, web-based, epidemiological tool for data entry and communication can also allow real-time , predictive, real-time mapping of any TB county outbreaks Precede –Proceed model may be employed by county-level public health officials in Florida to disseminate and prioritize county-level, TB model, epidemiological, information to their constituents. In so doing, factors regulating outbreaks of county-level TB may be accurately identified.

This thesis is focused on the question whether implementing of a malus policy into a bonus-malus system encourages drivers to be more responsible on the road. The drivers should be motivated to change their behaviour be aimed at decrease of reported insurance events because this system increases their premium whenever they are in an accident. To answer the given problem I used regression analysis with a random effects model and I analysed the drivers registered at insurance company Allianz in years 2000 - 2005. The result of this analysis shows that there was no change in the behaviour since the malus policy was introduced. The examined impact has been detected in case which was interacting with a different variable. The malus policy had the impact on a reduction of accidents in regions below 10 000 inhabitants. This thesis is also focused on the influence of driver characteristic and technical properties of his vehicle on reported insurance events. Tested variables are sex of the drivers, region of driver's residence, age and engine capacity. All explanatory variables have effect on the reported insurance events.

Human migration, the movement of people from one place to another with intention of settling there temporarily or permanently, is an integral part of development of human society. The beginning of the Industrial Revolution in late 18th century has resulted in economic growth and improvement of living standards. Countries, in which was industrialisation most intense attracted most immigrants. Second wave of migration was in second half of 20th century caused by development of communication technologies, which gave opportunities to less developed countries improve their economic development. This diploma thesis is aimed exactly on this period. Its goal is to research the impact of migration on economic growth and find out if the flow of migrants is beneficial for the economy or the exact opposite. The impact is examined by regression analysis on panel data for almost 200 hundred countries from whole world for time period 1955-2004.

Práce se zabývá longitudinálními daty - měřeními, která jsou prová- děna opakovaně na stejných subjektech. Popisuje r·zné typy model·, které jsou vhodné pro jejich analýzu. Postupuje od nejjednodušších lineárních model· s pevnými nebo náhodnými efekty, přes lineární a nelineární modely se smíšenými efekty, až ke zobecněným lineárním model·m a generalized estimating equati- ons (GEE). Vždy je uveden tvar modelu a zp·sob odhadu parametr·. Jednotlivé modely jsou také porovnávány mezi sebou. Teoretické poznatky jsou doplněny aplikacemi na reálná data. Pomocí lineárních model· analyzujeme data o výrobě v USA, nelineární modely využijeme k vysvětlení závislosti koncentrace léčiva v krvi na čase a GEE aplikujeme na data týkající se dýchacích potíží u dětí. 1