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The dependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial elements of risk management for an insurance portfolio. In this work, we show that the Sarmanov family of multivariate distribution can be used for dependent lines of business using a rank-based method estimation. In fact, an inadequate choice of the dependence structure may negatively impact the estimation of the marginals, which might lead to an undesirable effect on reserve computation. Thus, we propose a two-stage inference strategy in this thesis. We show that this strategy leads to robust estimation and better capture the dependence between the risks. We also show that it leads to smaller risk capital and a better diversification benefit. We introduce the two-stage inference using the Sarmanov distribution. First, we fit the marginals with generalized linear models (GLMs) and obtain the corresponding residuals. Secondly, the Sarmanov family of bivariate distributions links these marginals through the rank of residuals. We also show that this can be extended to a multivariate case. To illustrate this method, we analyzed two sets of data. For the bivariate case, we considered an insurance portfolio consisting of personal and commercial auto lines provided by a major US property-casualty insurer. We also used the data from three lines of business of a large Canadian insurance company for the multivariate dependence case. / Thesis / Master of Science (MSc)

Bayesian statistical methods have long been computationally out of reach because the analysis often requires integration of high-dimensional functions. Recent advancements in computational tools to apply Markov Chain Monte Carlo (MCMC) methods are making Bayesian data analysis accessible for all statisticians. Two such computer tools are Win-BUGS and SASR 9.2's PROC MCMC. Bayesian methodology will be introduced through discussion of fourteen statistical examples with code and computer output to demonstrate the power of these computational tools in a wide variety of settings.

With increased data sharing and research collaboration options available through modern technology, there is an increased need to find more advanced techniques to analyze data across multiple studies. A systematic method of pooling participant-level versus study-level data would be particularly valuable as it would allow for more complex statistical analyses, broader assessment of constructs, and a cost effective way to examine new questions and replicate previous findings. One notable difficulty in pooling raw data in the behavioral sciences is the heterogeneity in methodologies and consequent need to establish measurement invariance. The present study explores the feasibility of using Integrative Data Analysis (IDA) to combine 10 heterogeneous eating disorder prevention data sets and establish measurement invariance across the constructs of thin ideal internalization and body dissatisfaction. Using standard multiple groups factor analysis and likelihood-ratio tests to examine differential item functioning, separate one-factor models were established for the three measures used across studies. Partial measurement invariance was established for all measures. Implications for future IDA studies based on this process are discussed, particularly regarding the clinical impact of measurement invariance.

The purpose of this study was to explore to what extent interactive visualizations are applicable in the beer brewing domain specific area by measuring how the use of interactive visualizations can aid beer brewers in completing tasks related to their artisanal craft. The approach taken in researching said topic was through the cooperative collaborative creation of an interactive visualization dashboard system intended for the use in production brewing. This was created through the aid of previously proposed design frameworks and creation strategies that focused on the collaboration between visualization expert and domain specific partner. A visualization dashboard interface was created with thorough input from different brewers all around the world in the hopes of creating a general purpose tool that could indeed aid in the completion of creation and evaluation tasks. The final dashboard interface was scrutinized by brewers and received positive feedback in terms of its potential through evaluation sessions and personal interviews where the brewers were assigned tasks to complete. The completion of simple tasks such as quick recipe cognition and creation, batch parameter comparisons and fermentation tank analysis were all achieved while things such as variable fermentation analysis and reproduction evaluation strategies were more difficult. The conclusion drawn from the project was that the use of interactive visualizations can indeed aid and enhance beer brewer’s capabilities in completing some tasks but as per the altered frameworks used, it requires thorough collaboration from more than just the domain specific partners in the creation process of said visualizations in order to create the optimal tool. / Avsikten med denna studie är att utforska i vilken grad interaktiva visualiseringar kan tillämpas inom den domänspecifika ölbryggeri-industrin där visualiseringens syfte är att stödja utförandet av bryggningsrelaterade uppgifter. Ett samarbete startades, där ett antal bryggerier runt omkring i världen deltog och där dessa bryggerier delade med sig av sina bryggningsmetoder och relaterade data. Tillsammans med bryggare togs specialdesignade visualiseringsmoduler fram som kan användas för att utföra uppgifter tillsammans med dessa domänspecifika experter. En stor förstudie gjordes med hjälp av existerande utvärderingsramverk samt olika visualiseringsstrategier där fokus lades på samarbetet mellan visualiseringsexperter och ölexperter. Den slutliga visualiseringen som skapades var i form av en instrumentbräda där användaren får en unik insyn i data samt nya möjligheter att utforska datat på ett enkelt och smidigt sätt. Bryggare fick testa verktyget i en kontrollerad miljö och såg stora möjligheter för att ett sådant verktyg skulle kunna fungera bra i en produktionsmiljö förutsatt att man får en grundläggande utbildning i att använda verktyget till sin fulla potential.

For companies that supply a service to their customers, there will always be a difference in engagement from customer to customer. Some customers might have created an account, they might also have a paid subscription, or they neither have an account nor are a subscriber. Meaning that they only interact with the free content provided. Looking at this from the perspective of the company, a more engaged customer will generate more value than one less engaged. Therefore, it would be beneficial to try and understand why some customers are more engaged than others. This project purpose is to create a customer relationship management (CRM) pipeline for determining what it is that make some users more engaged with a service than other. The findings will be presented to Bonnier that they may use to try and increase customer engagement across the board. This is achieved by clustering users based on their recency, frequency, and volume (RFV) data with the help of two clustering algorithms. The clustering algorithms are evaluated to detriment which is more suited as well as to determine the optimal number of clusters. Lastly the clusters are analysed on different metrics connected to the users as well as to the articles they read. The results show some implications into what makes some users more engaged. It’s also concluded that more clustering and analysis would be needed to get even more insight into what leads to more customer engagement.

This research is concerned with examining market segments and regional variations associated with winery visitors in the state of Virginia. The tourism literature published by the state of Virginia for wineries indicates that there are five wine regions. In this research, data were collected from interviews conducted at wineries in each of the five wine regions. The first phase of analysis sought to create market segments using a factor-cluster approach. Segments were created using cluster analysis and multiple discriminant analysis. Three distinct market segments based on benefits sought by the visitor emerged from these data. Regional variations were examined in the second part of this study. The data were classified into individual regions based on the locations of the wineries examined. Distinct differences in the regional profiles were revealed. Weak significant relationships among the segments and regions were also revealed through analysis indicating a spatial component to the segments. This research proposes the use of three regions for market research purposes, while retaining the five existing regions for promoting an organized structure to visiting wineries in the state. / Master of Science

No / Long glass fibre (LGF) composites are extensively used in manufacturing to produce components with enhanced mechanical properties. Long fibres with length 12 to 25mm are added to a thermoplastic matrix. However severe fibre breakage can occur in the injection moulding process resulting in shorter fibre length distribution (FLD). The majority of this breakage occurs due to the melt experiencing extreme shear stress during the preparation and injection stage. Care should be taken to ensure that the longer fibres make it through the injection moulding process without their length being significantly degraded. This study is based on commercial 12 mm long glass-fibre reinforced polypropylene (PP) and short glass fibre Nylon. Due to the semi-flexiable behaviour of long glass fibres, the fibre orientation distribution (FOD) will differ from the orientation distribution of short glass fibre in an injection molded part. In order to investigate the effect the change in fibre length has on the fibre orientation distribution or vice versa, FOD data was measured using the 2D section image analyser. The overall purpose of the research is to show how the orientation distribution chnages in an injection moulded centre gated disc and end gated plaque geometry and to compare this data against fibre orientation predictions obtained from Autodesk Moldflow Simulation Insight.

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This dissertation addresses the challenge of enabling accurate analysis of network data while ensuring the protection of network participants' privacy. This is an important problem: massive amounts of data are being collected (facebook activity, email correspondence, cell phone records), there is huge interest in analyzing the data, but the data is not being shared due to concerns about privacy. Despite much research in privacy-preserving data analysis, existing technologies fail to provide a solution because they were designed for tables, not networks, and cannot be easily adapted to handle the complexities of network data. We develop several technologies that advance us toward our goal. First, we develop a framework for assessing the risk of publishing a network that has been "anonymized." Using this framework, we show that only a small amount of background knowledge about local network structure is needed to re-identify an "anonymous" individual. This motivates our second contribution: an algorithm that transforms the structure of the network to provably lower re-identification risk. In comparison with other algorithms, we show that our approach more accurately preserves important features of the network topology. Finally, we consider an alternative paradigm, in which the analyst can analyze private data through a carefully controlled query interface. We show that the degree sequence of a network can be accurately estimated under strong guarantees of privacy.