Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.

According to production process theory, customization is directly related to cost and inversely related to volume, efficiency, and productivity. However, customers generally desire products that are individually tailored to their wants and needs. For this reason, as residential contractors grow, they struggle to meet customers' demands for flexibility. This struggle to increase customization is not unique to the construction industry and many other industries have studied this problem in depth. While the inverse relationship between customization and cost is generally true, mass customization can enable increased customization with limited or no increased cost. The residential construction process employs many mass customization enabling principles, including modularity and product family design. However, the preconstruction process fails to employ these same principles. The purpose of this study was to explore how mass customization principles can simplify customization in the residential preconstruction process. Two rounds of interviews were conducted with residential construction industry preconstruction experts. Using their input, a process for implementing mass customization was developed. The results demonstrate that implementing mass customization principles can greatly simplify the purchasing, estimating, and option pricing processes for residential contractors. However, mass customization also significantly affects company structure, cost control strategies, trade relationships, and leanness. This research is enlightening to residential contractors struggling to manage customization. It also provides direction for software developers targeting the residential construction processes.

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Acute respiratory infections (ARI) caused by influenza and other respiratory viruses affect millions of people annually. Although usually self-limiting a more complicated or severe course may occur in previously healthy people but are more likely in individuals with underlying illnesses. The most common viral agent is rhinovirus whereas influenza is less frequent but is well known to cause winter epidemics. In primary care, rapid diagnosis of influenza virus infections is essential in order to provide treatment. Clinical presentations vary among the different pathogens but may overlap and may also depend on host factors. Predictive models have been developed for influenza but study results may be biased because only individuals presenting with fever were included. Most of these models have not been adequately validated and their predictive power, therefore, is likely overestimated. The main objective of this thesis was to compare different mathematical models for the derivation of clinical prediction rules in individuals presenting with symptoms of ARI to better distinguish between influenza, influenza A subtypes and entero-/rhinovirus-related illness in children and adults and to evaluate model performance by using data-splitting for internal validation. Data from a completed prospective cluster-randomized trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first episodes per season of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were randomly split by subjects into a derivation (2/3 of the dataset) and a validation population (1/3 of the dataset). All predictive models were developed in the derivation sets. Demographic factors and the classical symptoms of ARI were evaluated with logistic regression and Cox proportional hazard models using forward stepwise selection applying robust estimators to account for non-independent data and by means of recursive partitioning. The beta coefficients of the independent predictors were used to develop different point scores. These scores were then tested in the validation groups and performance between validation and derivation set was compared using receiver operating characteristics (ROC) curves. We determined sensitivities and specificities, positive and negative predictive values, and likelihood ratios at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate with areas under the receiver operating characteristic curves between 0.75 and 0.80 for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, performance of the models was slightly overestimated due to potential clustering of data and the results would first needed to be validated in a different population before application in clinical practice. / Thesis / Master of Science (MSc) / Every year, millions of people are attacked by "the flu" or the common cold. Certain signs and symptoms apparently are more discriminative between the common cold and the flu. However, the decision between starting a simple symptom orientated treatment, treating empirically for influenza or ordering a rapid diagnostic test that has only moderate sensitivity and specificity can be challenging. This thesis, therefore, aims to help physicians in their decision-making process by developing simple scores and decision trees for the diagnosis of influenza versus non-influenza respiratory infections. Data from a completed trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first seasonal episodes of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were split into a derivation and a validation set (=holdout group). Different mathematical models were applied to the derivation set and demographic factors as well as the classical symptoms of ARI were evaluated. The scores generated from the most important factors that remained in the model were then tested in the validation group and performance between validation and derivation set was compared. Accuracy was determined at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, the results would first needed to be validated in a different population before application in clinical practice.

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Целью работы является разработка методического подхода к оценке интеллектуального капитала металлургических предприятий Свердловской области. В основе методического подхода – расчет отдельных коэффициентов и интегрального показателя, характеризующих уровень развития структурных элементов интеллектуального капитала металлургического предприятия (человеческий, структурный и клиентский капиталы). Разработанный методический подход позволяет провести оценку показателей интеллектуального капитала по отдельности и в комплексе и использовать полученные данные в качестве аналитической основы для принятия стратегических решений в целях определения направления развития металлургического предприятия в условиях неопределенности внешней среды. / The aim of the work is to develop a methodical approach to assessing the intellectual capital of metallurgical enterprises in the Sverdlovsk region. The methodical approach is based on the calculation of individual coefficients and an integral indicator characterizing the level of development of the structural elements of the metallurgical enterprise’s intellectual capital (human, structural and client capital). The developed methodical approach makes it possible to assess the indicators of intellectual capital individually and in combination and use the data obtained as an analytical basis for making strategic decisions in order to determine the direction of development of a metallurgical enterprise in conditions of uncertainty external environment.

The problem of high proportion of zeroes has long been an interest in data analysis and modeling, however, there are no unique solutions to this problem. The solution to the individual problem really depends on its particular situation and the design of the experiment. For example, different biological, chemical, or physical processes may follow different distributions and behave differently. Different mechanisms may generate the zeroes and require different modeling approaches. So it would be quite impossible and inflexible to come up with a unique or a general solution. In this dissertation, I focus on cases where zeroes are produced by mechanisms that create distinct sub-populations of zeroes. The dissertation is motivated from problems of chronic toxicity testing which has a data set that contains a high proportion of zeroes. The analysis of chronic test data is complicated because there are two different sources of zeroes: mortality and non-reproduction in the data. So researchers have to separate zeroes from mortality and fecundity. The use of mixture model approach which combines the two mechanisms to model the data here is appropriate because it can incorporate the mortality kind of extra zeroes. A zero inflated Poisson (ZIP) model is used for modeling the fecundity in <i> Ceriodaphnia dubia</i> toxicity test. A generalized estimating equation (GEE) based ZIP model is developed to handle longitudinal data with zeroes due to mortality. A joint estimate of inhibition concentration (ICx) is also developed as potency estimation based on the mixture model approach. It is found that the ZIP model would perform better than the regular Poisson model if the mortality is high. This kind of toxicity testing also involves longitudinal data where the same subject is measured for a period of seven days. The GEE model allows the flexibility to incorporate the extra zeroes and a correlation structure among the repeated measures. The problem of zero-heavy data also exists in environmental studies in which the growth or reproduction rates of multi-species are measured. This gives rise to multivariate data. Since the inter-relationships between different species are imbedded in the correlation structure, the study of the information in the correlation of the variables, which is often accessed through principal component analysis, is one of the major interests in multi-variate data. In the case where mortality influences the variables of interests, but mortality is not the subject of interests, the use of the mixture approach can be applied to recover the information of the correlation structure. In order to investigate the effect of zeroes on multi-variate data, simulation studies on principal component analysis are performed. A method that recovers the information of the correlation structure is also presented. / Ph. D.

Ce manuscrit est structuré en deux parties. La première partie composée des chapitres 2à 4 aborde le problème d'estimation du nombre de classes dans une population avec une application en écologie. La deuxième partie, correspondant au chapitre 5,concerne la mise en oeuvre de méthodes statistiques pour analyser des données de pêche. Dans la première partie, nous considérons une population hétérogène subdiviséeen plusieurs classes. À partir d'un échantillon, les effectifs d'individus observés parclasse, encore appelés abondances, sont utilisés pour estimer le nombre total declasses dans la population. Dans la littérature consacrée à l'estimation du nombrede classes, les méthodes basées sur un mélange de distributions de Poisson semblentêtre les plus performantes (voir par exemple les travaux de Chao and Bunge (2002)dans le cadre paramétrique et celui de Wang and Lindsay (2005) dans un cadrenon paramétrique). La mise en oeuvre de ces approches sur des données réellesmet en évidence que la distribution des abondances peut être approchée par unedistribution convexe. Nous proposons une approche non paramétrique pour estimerla distribution des abondances sous contrainte de convexité. Cette contrainte définitun cadre théorique d'estimation d'une densité discrète. Le problème d'estimation dunombre de classes est donc abordé en deux volets. Nous montrons d'une part l'existenceet l'unicité d'un estimateur d'une densité discrète sous la contrainte de convexité.Sous cette contrainte, nous démontrons qu'une densité discrète s'écrit comme un mélange de densités triangulaires. À partir de l'algorithme de réduction du supportproposé par Groeneboom et al. (2008), nous proposons un algorithme exact pourestimer les proportions dans le mélange. D'autre part, la procédure d'estimationd'une densité discrète convexe nous sert de cadre pour l'estimation de la distributiontronquée en zéro des observations d'abondance. L'estimation de la loi tronquée obtenue est ensuite prolongée en zéro pour estimer la probabilité qu'une classe ne soit pasobservée. Ce prolongement en zéro est fait de façon à annuler la proportion dela première composante dans le mélange de densités triangulaires. Nousaboutissons à une estimation du nombre de classes à l'aide d'un modèle binomial ensupposant que chaque classe apparaît dans un échantillon par une épreuve deBernoulli. Nous montrons la convergence en loi de l'estimateur proposé. Sur le plan pratique, une application aux données réelles en écologie est présentée. La méthode est ensuite comparée à d'autres méthodes concurrentes à l'aide de simulations. La seconde partie présente l'analyse des données de pêche collectées dans le fleuveOuémé au Bénin. Nous proposons une démarche statistique permettant de regrouperles espèces selon leur profil temporel d'abondances, d'estimer le stock d'une espèceainsi que leur capturabilité par les engins de pêche artisanale. / This manuscript is structured in two parts. The #rst part composed of Chapters 2to 4 deals with the problem of estimating the number of classes in a population withan application in ecology. The second part, corresponding to Chapter 5, concernsthe application of statistical methods to analyze fisheries data.In the first part, we consider a heterogeneous population split into several classes.From a sample, the numbers of observed individuals per class, also called abun-dances, are used to estimate the total number of classes in the population. In theliterature devoted to the number of classes estimation, methods based on a mix-ture of Poisson distributions seem to be the most effcient (see for example the workof Chao and Bunge (2002) in the parametric framework and that of Wang and Lind-say (2005) in a non-parametric framework). Applications of these approaches to realdata show that the distribution of abundances can be approximated by a convexdistribution. We propose a non-parametric approach to estimate the distribution ofabundances under the constraint of convexity. This constraint defines a theoreticalframework for estimating a discrete density. The problem of estimating the numberof classes is then tackled in two steps.We show on the one hand the existence and uniqueness of an estimator of adiscrete density under the constraint of convexity. Under this constraint, we provethat a discrete density can be written as a mixture of triangular distributions. Usingthe support reduction algorithm proposed by Groeneboom et al. (2008), we proposean exact algorithm to estimate the proportions in the mixture.On the other hand, the estimation procedure of a discrete convex density is usedto estimate the zero-truncated distribution of the observed abundance data. Thezero-truncated distribution estimate is then extended at zero to derive an estimateof the probability that a class is not observed. This extension is made so as tocancel the first component in the mixture of triangular distributions. An estimateof the total number of classes is obtained through a binomial model assuming thateach class appears in a sample by a Bernoulli trial. We show the convergence inlaw of the proposed estimator. On practical view, an application to real ecologicaldata is presented. The method is then compared to other concurrent methods usingsimulations.The second part presents the analysis of fisheries data collected on the Ouémériver in Benin. We propose a statistical approach for grouping species accordingto their temporal abundance profile, to estimate the stock of a species and theircatchability by artisanal fishing gears.

In the presented thesis the issue of dependency between response variables within the subjects in the generalized linear models framework is investigated. Reserving in non-life insurance is a key factor for the financial position of a company. The text introduces the basic actuarial notation, terminology and methods. The main part is focused on panel data framework, especially Generalized Linear Mixed Models (GLMM) as well as Generalized Estimating Equations (GEE), and their application on claims reserving. The aim of this thesis is to show the advantages, disadvantages, limitations and the comparison of these approaches on representative datasets, which were chosen according to results obtained from whole database analysis. Significant focus is on model selection and diagnostics used for this purpose. Finally, the obtained results are summarized in tables, figures and the comparison of the methods is provided. Powered by TCPDF (www.tcpdf.org)