This June one of the biggest and most popular sports tournaments will take place in Austria and Switzerland, the European soccer championship 2008 (UEFA EURO 2008). Therefore millions of soccer fans in Europe and throughout the world are asking themselves: "Who is going to win the EURO 2008?" Many people, including sports experts and former players, give their guesses and expectations in the media, but there is also a group with financial incentives, like some economists who expect economical increases for the country of the winning team and bookmakers and their customers who directly make money with their beliefs. Some predictions are only guesses, but other predictions are based on quantitative methods, such as the studies of UBS Wealth Management Research Switzerland and the Raiffeisen Zentralbank. In this report we will introduce a new method for predicting the winner. Whereas other prediction methods are based on historical data, e.g., the Elo rating, or the FIFA/Coca Cola World rating, our method is based on current expectations, the bookmakers odds for winning the championship. In particular we use the odds for winning the championship for each of the 16 teams of 45 international bookmakers. By interpreting these odds as rating of the expected strength of the teams by the bookmakers, we derive a consensus rating by modelling the log-odds using a random-effects model with a team-specific random effect and a bookmaker-specific fixed effect. The consensus rating of a team can be used as an estimator for the unknown "true" strength of a team. Our method predicts team Germany with a probability of about 18.7% as the EURO 2008 winner. We predict also that the teams playing the final will be Germany and Spain with a probability of 13.9%, where Germany will win with a probability of 55%. In our study, Italy, the favorite according to the current FIFA/Coca Cola World ranking and Elo ranking, has a much lower probability than these teams to win the tournament: only 10.6%. The defending champion Greece has low chances to win the title again: about 3.4%. Furthermore, the expected performance of the host countries, Austria and Switzerland, is much better in the bookmakers consensus than in the retrospective Elo and FIFA/Coca Cola World ratings, i.e., indicating an (expected) home court advantage. Despite the associated increase in the winning probabilities, both teams have rather poor chances to win the tournament with probabilities of 1.3% and 4.0%, respectively. In a group effect study we investigate how much the classification into the four groups (A-D) affects the chance for a team to win the championship. / Series: Research Report Series / Department of Statistics and Mathematics

Genome-wide association studies (GWAS) have successfully identified thousands of variants robustly associated with hundreds of complex traits, but the biological mechanisms driving these results remain elusive. Functional annotation, describing the roles of known genes and regulatory elements, provides additional information about associated variants. This dissertation explores the potential of these annotations to explain the biology behind observed GWAS results. The first project develops a random-effects approach to genetic fine mapping of trait-associated loci. Functional annotation and estimates of the enrichment of genetic effects in each annotation category are integrated with linkage disequilibrium (LD) within each locus and GWAS summary statistics to prioritize variants with plausible functionality. Applications of this method to simulated and real data show good performance in a wider range of scenarios relative to previous approaches. The second project focuses on the estimation of enrichment by annotation categories. I derive the distribution of GWAS summary statistics as a function of annotations and LD structure and perform maximum likelihood estimation of enrichment coefficients in two simulated scenarios. The resulting estimates are less variable than previous methods, but the asymptotic theory of standard errors is often not applicable due to non-convexity of the likelihood function. In the third project, I investigate the problem of selecting an optimal set of tissue-specific annotations with greatest relevance to a trait of interest. I consider three selection criteria defined in terms of the mutual information between functional annotations and GWAS summary statistics. These algorithms correctly identify enriched categories in simulated data, but in the application to a GWAS of BMI the penalty for redundant features outweighs the modest relationships with the outcome yielding null selected feature sets, due to the weaker overall association and high similarity between tissue-specific regulatory features. All three projects require little in the way of prior hypotheses regarding the mechanism of genetic effects. These data-driven approaches have the potential to illuminate unanticipated biological relationships, but are also limited by the high dimensionality of the data relative to the moderate strength of the signals under investigation. These approaches advance the set of tools available to researchers to draw biological insights from GWAS results.

Student mobility is a common phenomenon in longitudinal data in educational research. The characteristics of education longitudinal data create a problem for the conventional multilevel model. Grady and Beretvas (2010) introduced a cross-classified multiple membership growth curve (CCMM-GCM) model to handle Student mobility over time by capturing complex higher level clustering structure in the data. There are some limitations in the CCMM-GCM model. By creating dummy coded indicators for each measurement occasion, the new model can improve the accuracy and provides an easier and more flexible structure at the higher level. This study provides some support that the new model better fits a dataset than the CCMM-GCM model / text

The present study investigated estimation of the variance of the cross-classified factors’ random effects’ interaction for cross-classified data structures. Results for two different three-level cross-classified random effects model (CCREM) were compared: Model 1 included the estimation of this variance component and Model 2 assumed the value of this variance component was zero and did not estimate it. The second model is the model most commonly assumed by researchers utilizing a CCREM to estimate cross-classified data structures. These two models were first applied to a real world data set. Parameter estimates for both estimating models were compared. The results for this analysis served as a guide to provide generating parameter values for the Monte Carlo simulation that followed. The Monte Carlo simulation was conducted to compare the two estimating models under several manipulated conditions and assess their impact on parameter recovery. The manipulated conditions included: classroom sample size, the structure of the cross-classification, the intra-unit correlation coefficient (IUCC), and the cross-classified factors’ variance component values. Relative parameter and standard error bias were calculated for fixed effect coefficient estimates, random effects’ variance components, and the associated standard errors for both. When Model 1 was used to estimate the simulated data, no substantial bias was found for any of the parameter estimates or their associated standard errors. Further, no substantial bias was found for conditions with the smallest average within-cell sample size (4 students). When Model 2 was used to estimate the simulated data, substantial bias occurred for the level-1 and level-2 variance components. Several of the manipulated conditions in the study impacted the magnitude of the bias for these variance estimates. Given that level-1 and level-2 variance components can often be used to inform researchers’ decisions about factors of interest, like classroom effects, assessment of possible bias in these estimates is important. The results are discussed, followed by implications and recommendations for applied researchers who are using a CCREM to estimate cross-classified data structures. / text

The purpose of the simulation was to assess the impact of weight pattern assignment when using the multiple membership random effects model (MMREM). In contrast with most previous methodological research using the MMREM, mobility was not randomly assigned; rather the likelihood of student mobility was generated as a function of the student predictor. Two true weights patterns were used to generate the data (random equal and random unequal). For each set of generated data, the true correct weights and two incorrect fixed weight patterns (fixed equal and fixed unequal) that are similar to those used in practice by applied researchers were used to estimate the model. Several design factors were manipulated including the percent mobility, the ICC, and the true generating values of the level one and level two mobility predictors. To assess parameter recovery, relative parameter bias was calculated for the fixed effects and random effects variance components. Standard error (SE) bias was also calculated for the standard errors estimated for each fixed effect. Substantial relative parameter bias differences between weight patterns used were observed for the level two school mobility predictor across conditions as well as the level two random effects variance component, in some conditions. Substantial SE bias differences between weight patterns used were also found for the school mobility predictor in some conditions. Substantial SE and parameter bias was found for some parameters for which it was not anticipated. The results, discussion, future directions for research, and implications for applied researchers are discussed.

This thesis contributes to the under-researched field of political selection, namely one’s re-selection onto the party list after one has been elected in the previous term. The theoretical rationale is to introduce a broader concept of political selection to a field mostly focused on political recruitment, one’s first point of entry into Politics. We show that the framework developed to study political recruitment can be adapted to study any kind of political selection that involves a broad pool of aspirants from which successful candidates must be selected. To this end, we utilise a panel dataset containing data on 387 Czech legislators covering the period between 1996 and 2013. Using fixed- and random-effects panel models, we show that voting along the party line and preferential vote share at time t are strong predictors of getting a better party-list rank at t+1. Legislative experience, however, is negatively associated with how well one fares at the re-selection process. We also provide evidence that it is left-wing parties rather than their right-wing counterparts that discriminate against women at the re-selection stage. The study thus contributes, directly or otherwise, to debates on women’s representation, political careers and re-election.

京都大学 / 新制・論文博士 / 博士(社会健康医学) / 乙第13422号 / 論社医博第16号 / 新制||社医||11(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 佐藤 俊哉, 教授 藤渕 航, 教授 黒田 知宏 / 学位規則第4条第2項該当 / Doctor of Public Health / Kyoto University / DFAM

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One area of forest biometrics that continues to progress is the development of statistical models as tools to describe tree taper. Taper models allow for the prediction of multiple tree level attributes including: diameter at any height, total tree stem volume, merchantable volume and height to any upper stem diameter from any lower stem height, individual log volumes, and subsequently total tree value. In this work, we generalize segmented regression taper models to include multiple segments and compare it to the traditional 3-Segment (2-Knot) models commonly observed in the forestry literature. We then focus on predicting a future realization of diameter given previously observed data. This is accomplished by comparing a segmented taper model under both the Generalized Algebraic Difference Approach (GADA) and Nonlinear Mixed Effects Models (NLMM) methodologies. Both the GADA and NLMM allow for predictions at the individual tree level of a future realization diameter given a differing number of observed height-diameter pairs. Finally, we explore the prediction and cost/benefit of total tree volume obtained from an integrated taper equation with the incorporation of tree specific random effects given differing observed height-diameter pairs.

Reliability experiments provide important information regarding the life of a product, including how various factors may affect product life. Current analyses of reliability data usually assume a completely randomized design. However, reliability experiments frequently contain subsampling which is a restriction on randomization. A typical experiment involves applying treatments to test stands, with several items placed on each test stand. In addition, raw materials used in experiments are often produced in batches. In some cases one batch may not be large enough to provide materials for the entire experiment and more than one batch must be used. These batches lead to a design involving blocks. This dissertation proposes two methods for analyzing reliability experiments with random blocks and subsampling. The first method is a two-stage method which can be implemented in software used by most practitioners, but has some limitations. Therefore, a more rigorous nonlinear mixed model method is proposed. / Ph. D.