This thesis explores stochastic modeling and Bayesian inference strategies in the context of the following three problems: 1) Modeling the complex interactions between and within molecules; 2) Extracting information from stepwise signals that are commonly found in biophysical experiments; 3) Improving the computational efficiency of a non-parametric Bayesian inference algorithm. Chapter 1 studies the data from a recent single-molecule biophysical experiment on enzyme kinetics. Using a stochastic network model, we analyze the autocorrelation of experimental fluorescence intensity and the autocorrelation of enzymatic reaction times. This chapter shows that the stochastic network model is capable of explaining the experimental data in depth and further explains why the enzyme molecules behave fundamentally differently from what the classical model predicts. The modern knowledge on the molecular kinetics is often learned through the information extracted from stepwise signals in experiments utilizing fluorescence spectroscopy. Chapter 2 proposes a new Bayesian method to estimate the change-points in stepwise signals. This approach utilizes marginal likelihood as the tool of inference. This chapter illustrates the impact of the choice of prior on the estimator and provides guidelines for setting the prior. Based on the results of simulation study, this method outperforms several existing change-points estimators under certain settings. Furthermore, DNA array CGH data and single molecule data are analyzed with this approach. Chapter 3 focuses on the optional Polya tree, a newly established non-parametric Bayesian approach (Wong and Li 2010). While the existing study shows that the optional Polya tree is promising in analyzing high dimensional data, its applications are hindered by the high computational costs. A heuristic algorithm is proposed in this chapter, with an attempt to speed up the optional Polya tree inference. This study demonstrates that the new algorithm can reduce the running time significantly with a negligible loss of precision. / Statistics

Made available in DSpace on 2016-06-02T20:05:59Z (GMT). No. of bitstreams: 1 DissFMTP.pdf: 1395799 bytes, checksum: 92e515da8b8dec81124d070a8fec7082 (MD5) Previous issue date: 2007-09-27 / In several research areas, the study of the occurrence of a phenomenon over a period of time is very common. In this case, if we use the theory of Generalized Linear Models to analyze the subject of interest, we ll have, as a consequence, incorrect inferences concerning regression parameters and inefficient estimators, since considering the random variables as independent responses, is the main characteristic of this theory. When the variable response is observed over time, there can be a correlation between the observations and that must be taken into consideration on the estimation of the parameters. To incorporate this temporal dependence, we can use the theory of Generalized Estimating Equations, proposed by Liang & Zeger, 1986, as an extension of the Generalized Linear Models, to compute the correlation between the observations. Besides the temporal correlation, there can even be a space correlation and, in this case, we can use the theory of Geostatistic to estimate the reach of the correlation of samples over a study region, as well as to identify whether there is a privileged direction of variability of the studied phenomenon, important data not revealed when we utilize the theories of classic statistic. In this dissertation, we applied the methodologies mentioned above to try to explain the presence and the behavior of the female Aedes (Stegomyia) aegypti captured by adulticed traps in the city of Mirassol/SP, with the goal of helping on the search of more precise methods to contain the dissemination of dengue. / É muito comum, em diversas áreas, o estudo da ocorrência de um fenômeno ao longo do tempo. Neste caso, se utilizarmos a teoria de Modelos Lineares Generalizados para analisarmos o objeto de interesse, teremos como conseqüência inferências incorretas dos parâmetros regressores e estimadores ineficientes, uma vez que a principal característica desta teoria é considerar as variáveis aleatórias como sendo respostas independentes. Quando a variável resposta é observada ao longo do tempo, pode haver uma correlação entre as observações e isso deve ser levado em consideração na estimação dos parâmetros. Para incorporarmos esta dependência temporal podemos utilizar a teoria das Equações de Estimação Generalizadas, proposta por Liang & Zeger, 1986, como uma extensão dos Modelos Lineares Generalizados para computar a correlação existente entre as observações. Além da correlação temporal, pode haver, ainda, uma correlação espacial e, neste caso, podemos utilizar a teoria da Geoestatística para estimarmos o alcance de correlação das amostras ao longo de uma região de estudo, bem como para identificarmos se há uma direção privilegiada de variabilidade do fenômeno analisado, dados importantes não revelados quando utilizamos as teorias da estatística clássica. Nesta dissertação aplicamos as metodologias acima citadas para tentar explicar a presença e o comportamento de fêmeas Aedes (Stegomyia) aegypti capturadas por armadilhas adulticidas na cidade de Mirassol/SP, com o objetivo de colaborar na busca de métodos mais precisos para contenção da disseminação da dengue.

Introduction: Eating disorders are serious psychiatric disorders that often require specialized care. Associated psychiatric comorbidity is frequent, with the most common comorbid conditions being anxiety and mood disorders. Eriksbergsgården in Örebro is one of Sweden’s specialized eating disorder treatment units.Aim: Primary aims were to describe clinical characteristics of the adult patient group at Eriksbergsgården and to evaluate treatment outcome and patient satisfaction at the one-year follow-up. An additional aim was to examine if factors such as psychiatric comorbidity affected treatment outcome.Methods: This study used data from Riksät and Stepwise, two large-scale Swedish registers for eating disorder treatment. Data for this study was registered into Stepwise and Riksät at Eriksbergsgården between August 2010 and December 2017 and 489 adult patients of both genders constituted the study group. Patient characteristics and DSM-IV axis I psychiatric comorbidity were assessed at the initial evaluation. At the one-year follow-up, treatment outcome and patient satisfaction were evaluated.Results: The most common diagnoses in this patient material were eating disorder not otherwise specified, 56.6 %, followed by bulimia nervosa, 26.4 %. At the initial evaluation, 62.0 % of the patients suffered from psychiatric comorbidity. Of the patients with initial comorbidity, 43.3 % were recovered at the one-year follow-up, compared to 62.8 % of the patients with no initial comorbidity, p=0.021.Conclusion: Our results confirm the previously known fact that psychiatric comorbidity among eating disorder patients is common. Also, the results identify psychiatric comorbidity as a possible factor to have negative effect on the treatment outcome.

Datainsamling har genomförts i Tarfaladalen under 5 dagar vid månadsskiftet mellan mars och april 2017. Insamlingen resulterade i 36 BTS-mätningar (Bottom Temperature of Snow cover) som därefter har använts tillsammans med data från tidigare insamlingar, till att skapa en sammanställd modell över förekomsten av permafrost omkring Tarfala. En statistisk undersökning syftade till att identifiera meningsfulla parametrar som permafrost beror av, genom att testa de oberoende variablerna mot BTS i en stegvis regression. De oberoende faktorerna höjd över havet, aspekt, solinstrålning, vinkel och gradient hos sluttningar producerades för varje undersökt BTS-punkt i ett geografiskt informationssystem.                 Den stegvisa regressionen valde enbart höjden som signifikant variabel, höjden användes i en logistisk regression för att modellera permafrostens utbredning. Den slutliga modellen visade att permafrostens sannolikhet ökar med höjden. För att skilja mellan kontinuerlig, diskontinuerlig och sporadisk permafrost delades modellen in i tre zoner med olika sannolikhetsspann. Den kontinuerliga permafrosten är högst belägen och därav den zon där sannolikheten för permafrost är störst, denna zon gränsar till den diskontinuerliga permafrosten vid en höjd på 1523 m. Den diskontinuerliga permafrosten har en sannolikhet mellan 50–80 % och dess undre gräns på 1108 m.ö.h. separerar den diskontinuerliga zonen från den sporadiska permafrosten / A field data collection has been carried out in Tarfala valley at the turn of March to April 2017. The collection resulted in 36 BTS-measurements (Bottom Temperature of Snow cover) that has been used in combination with data from earlier surveys, to create a model of the occurrence of permafrost around Tarfala. To identify meaningful parameters that permafrost relies on, independent variables were tested against BTS in a stepwise regression. The independent variables elevation, aspect, solar radiation, slope angle and curvature were produced for each investigated BTS-point in a geographic information system.                 The stepwise regression selected elevation as the only significant variable, elevation was applied to a logistic regression to model the permafrost occurrence. The final model showed that the probability of permafrost increases with height. To distinguish between continuous, discontinuous and sporadic permafrost, the model was divided into three zones with intervals of probability. The continuous permafrost is the highest located zone and therefore has the highest likelihood, this zone delimits the discontinuous permafrost at 1523 m a.s.l. The discontinuous permafrost has probabilities between 50-80 % and its lower limit at 1108 m a.s.l. separates the discontinuous zone from the sporadic permafrost.

Le travail soumis commence par un aperçu du micro-crédit tel qu’il a été introduit au Bangladesh par M. Yunus. Puis on donne un modèle stochastique des retards de versement. Comme ces retards ne donnent pas lieu à une sanction financière, ils constituent, de fait, une baisse du taux réel de crédit. Ce taux est alors, lui-même, aléatoire. On calcule un taux espéré en fonction de la probabilité de retard de remboursement hebdomadaire. On déduit que ce taux espéré est d’environ 3.5% inférieur au taux (annoncé) du cas déterministe si l’on considère que 3% des retards atteignent 4 semaines. Le travail se poursuit par une étude statistique de données du micro-crédit en Thaïlande. On commence par présenter un modèle de régression logistique du taux de remboursement par rapport aux 23 variables mesurées sur un échantillon de 219 groupes d’emprunteurs. On présente ensuite une sélection des variables les plus pertinentes selon un critère AIC ou BIC par une méthode “backward stepwise”. Finalement des expériences sur des sous-échantillons montrent une bonne stabilité du choix des variables obtenues par la sélection. / This study is inspired from a real scenario of microcredit lending introduced in Bangladesh by Yunus. A stochastic model of random delays in repayment installments is then constructed. Since delays occur without financial penalty, the interest rate is obviously lower than the exact claimed. This rate then becomes a random variable corresponding to the random repayment time, in which simulation results of its distribution are provided. The expected rate is computed as a function of in-time installment probability. It is found around 3.5% lower than the exact one in the deterministic case when considering 3% of delay occurred within four weeks in real practice. The work is extended to a statistical analysis on data of microcredit in Thailand. It is started by presenting a logistic regression model of repayment outcome containing 23 input variables measured on a sample of 219 lending groups. Applying penalized criterion, AIC or BIC together with backward stepwise elimination procedure on the full model, a more parsimonious model kept only most relevant predictors is obtained. Finally, experiments on sub-samples show a stability of the chosen predictors obtained by the selection method.

Adverse drug reactions are unwanted effects alongside the intended benefit of a drug and might be responsible for 3-7\% of hospitalizations. Finding such reactions is partly done by analysing individual case safety reports (ICSR) of adverse events. The reports consist of categorical terms that describe the event.Data-driven identification of suspected adverse drug reactions using this data typically considers single adverse event terms, one at a time. This single term approach narrows the identification of reports and information in the reports is ignored during the search. If one instead assumes that each report is connected to a topic, then by creating a cluster of the reports that are connected to the topic more reports would be identified. More context would also be provided by virtue of the topics. This thesis takes place at Uppsala Monitoring Centre which has implemented a probabilistic model of how an ICSR, and its topic, is assumed to be generated. The parameters of the model are estimated with expectation maximization (EM), which also assigns the reports to clusters. The clusters are improved with Consensus Clustering that identify groups of reports that tend to be grouped together by several runs of EM. Additionally, in order to not cluster outlying reports all clusters below a certain size are excluded. The objective of the thesis is to improve the algorithm in terms of computational efficiency and quality, as measured by stability and clinical coherence. The convergence of EM is improved using stepwise EM, which resulted in a speed up of at least 1.4, and a decrease of the computational complexity. With all the speed improvements the speed up factor of the entire algorithm can reach 2 but is constrained by the size of the data. In order to improve the clusters' quality, the community detection algorithm Leiden is used. It is able to improve the stability with the added benefit of increasing the number of clustered reports. The clinical coherence score performs worse with Leiden. There are good reasons to further investigate the benefits of Leiden as there were suggestions that community detection identified clusters with greater resolution that still appeared clinically coherent in a posthoc analysis.

This work is devoted to seeking methods for analysis of survival data with the Aalen model under special circumstances. We supposed, that all regression functions and all covariates of the observed individuals were nonnegative and we named this class of models monotone Aalen models. To find estimators of the unknown regres- sion functions we considered three maximum likelihood based approaches, namely the nonparametric maximum likelihood method, the Bayesian analysis using Beta processes as the priors for the unknown cumulative regression functions and the Bayesian analysis using a correlated prior approach, where the regression functions were supposed to be jump processes with a martingale structure.

The dissertation focuses on the different determinants of education, their effects on the educational outcome, and the overall effect of education on the lifetime consequences. The first chapter focuses on the inequality of educational opportunity across different demographic factors. This chapter employs a broader set of social factors to provide fresh insights into the inequality situation in the USA relative to those of the extant literature. The chapter employs polynomial trends for the effects of social factors to identify long-term trends in the determinants of the differences in attainment of each of four achievements (high school graduation, some college, college graduation, and post-college work) across different endogenous social groups. Using the Panel Study of Income Dynamics (PSID) data for the years of 1968-2013, we show how inequality of educational opportunity and its determinants have evolved over the years. The chapter utilizes the machine-learning process and logistic regression model to identify inequality of opportunity. The second chapter examines the age demographic distribution of graduates across cohorts from 1940 until 1990. Using the PSID data, the paper explored the first and second moment of the age of graduating from high school and college across the US. To deal with the data deficiencies, a large part of the chapter dealt with data preparation. The chapter provides a unique method of extracting information on the graduating age of the individuals both from high school and from college. The results show a large dispersion across the full sample. The data truncated to a standard length, however, provides a much smaller dispersion and much smaller moments. The chapter concludes that as the time passes, people tend to attain education at a younger age. The third chapter investigates the trends of the contribution of different factors of income starting from 1910 cohort. Following Mincer (1974), a wave of papers studied how various factors contribute to the earnings of individuals. This paper contributes to that literature in three ways: (i) using the PSID data, it computes the actual working experience of the individuals, (ii) it studies the cohorts who were born in 1910 or afterwards, unlike the existing papers, and (iii) it adds two variables—technological progress and the occupation with which individuals start their careers—to an extended Mincerian equation. The results re-emphasize the importance of education in lifetime earnings. The results also show that while some of the determinants of income have become more important over the years, other factors have not changed much in importance. / PHD / The reason for choosing the theme ‘Evolution of the Determinants of Educational Attainment and its Consequences’ was to investigate the different determinants of education, their effects on the educational outcome, and the overall effect of education on the lifetime consequences. Education is considered as one of the tools to eradicate poverty. Yet, countries with high educational coverage keeps suffering from poverty, a reason for which is higher inequality of opportunity. In the first chapter, entitled ‘Inequality in Educational Opportunity in the United States’, opportunity inequality in education is illustrated. Much inequality stems from differences in educational attainment. A lack of educational attainment puts an individual behind in the career race, even before the race has started. While individuals are responsible for some of the differences in educational attainment, there are factors outside the control of individuals that play substantial roles. The inequality that arises from these factors is known as inequality of opportunity. This paper focuses on inequality of educational opportunity across socioeconomic background, race, and sex. The factors that are analyzed for their contributions to inequality of educational opportunity are father’s education, father’s occupation, mother’s education, and economic status of the individual’s family. The results show that inequality of opportunity has seen a consistent decline for high school completion. The inequality of opportunity (IO) declines for obtaining some college education for the bottom two social groups and remained persistent for the relatively more advantaged group. For college/post-college education, the IO is much lower and, in general, remained persistent across the social strata. Although the females were behind the males – given the equal opportunity – regardless of the race and socioeconomic status during the beginning and the mid twentieth century, the scenario reversed in the late twentieth century. In terms of educational disparity among races, African Americans trail their White counterparts along all the years. The second chapter ‘First and Second Moments of the Age Distributions of Graduates’ looks into the age characteristics (mean and variance) in graduating from high school and college across the cohorts from 1940s to 1990s. The idea of the paper largely came from the first chapter of the dissertation as we assumed the lack of opportunity at the earlier age could delay the attainment of education. The paper intends to find out the average age of graduation over the years. In the process, the paper put forward a method to extract the information of age of graduation from the Panel Study of Income Dynamics (PSID) data, as the database does not readily avail the information. The chapter concludes that as the time passes, people tend to attain education at a much younger age. Titled as ‘Factors Affecting Income: Education, Experience, and Beyond’, the third chapter investigates the contribution of different factors – education, experience, parental endowments, and labor market conditions – in the returns to education using the PSID data and compare the more recent scenarios with the past. This paper focuses on the trend of the rate of return to different factors of income across the two cohorts – those born between 1910 and 1950, and those born after 1950 – while identifying the changes in the returns for the same education level over time. The paper aims to find out how the contribution of the different factors of earning has changed in the USA over the years. The paper also intends to find out the role of technological progress in reducing the earning gaps across the different social groups. The results re-emphasize the importance of education in lifetime earnings. Experience has become a more important factor of income over the years. The chapter also suggests that income of an individual is a monotonic function of socioeconomic endowments and better endowments resulted in higher returns. Lastly, the chapter finds that the technological investment is progressive in manner.

Företag inom dagligvaruhandeln använder sig ofta av database marketing för att anpassa deras erbjudande till deras kunder och därmed stärka kundrelationen och ökaderas försäljning. Länge har logistisk regression varit en modell som ofta används för att bygga upp maskininlärningsmodeller som kan förutse vilka erbjudanden som löses in av vilken kund. I arbetet utvärderas en maskininlärningsmodell med logistisk regression och stepwise selection på kunddata från en av Sveriges större aktörer inom dagligvaruhandeln. Modellen jämförs med en annan modell som istället använder sig utav elastic net, vilket är en regulariserad regressionsmetod. Modellerna testas på fem olika produkter ur företagets sortiment och baseras på ett femtiotal variabler som beskriver kundernas sociodemografiska data och historiska köpbeteende i företagets butiker. Dessa utvärderas med hjälp av en förväxlingsmatris och värden för deras Accuracy, Balanced Accuracy, Precision, Recall och F1-score. Dessutom utvärderas modellen utifrån affärsnytta, påverkan på kundrelationer och hållbarhet. Studien visade att den logistiska regressionen med stepwise selection hade ett genomsnittligt värde för Precision på 23 procent. Vid användning av elastic net ökade värdet för Precision med i genomsnitt 7 procentenheter för samtliga modeller. Detta kan bero på att vissa av parametrarna i modellen med stepwise selection får överdrivet stora värden samt att stepwise selection väljer ut variabler för modellen som inte är optimala för att förutsäga kundens beteende. Det noterades även att kunder generellt verkade nöjda med de erbjudanden de fått, men missnöjda ifall de kände sig missförstådda av företaget. / Companies within the FMCG trade often uses database marketing to customize offers to each customer, and thereby strengthen customer relationships to the company and increase their sales. For a long time, logistic regression has been the preferred machine modelling method to predict which offer to present to each costumer. This study evaluates a machinelearning model based on logistic regression and stepwise selection on costumer data from one of Sweden’s larger companies within the FMCG trade. The model is later compared to another model based on the elastic net-method, which is a regularized regressionmodel. The models are tested on five different products from the company’s assortment and are based on about fifty different variables which describes the costumers’ sociodemographic factors and purchasing history. The models are evaluated using a confusion matrix and values stating their Accuracy, BalancedAccuracy, Precision, Recall and F1-score. Furthermore, the model is evaluated in the perspectives of business advantages, costumer relations and sustainability. The study concluded that the logistic regression and stepwise selection-model had an average Precisionon 23 procent. When the elastic net-method was used the Precision increased with approximately 7 percentage points. This might depend on the fact that some of the parameters in the logistic regression-model had an overrated value and that the stepwise selection chose a subset of features that was not optimal to predict the consumer behaviour. It was also noted that costumers most often seemed content, but were dissatisfied if they felt misunderstood by the company.

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