Syftet med denna uppsats är att undersöka hur olika aspekter av arbetslivet påverkar individen. Vår utgångspunkt i denna uppsats är att vi ställer oss kritiska till arbeten med högt självbestämmande som blir allt mer vanligt. Till vår hjälp har vi en mängd tidigare forskning samt kritisk teori och Foucaults teori om makt och övervakning som guidar oss i genom forskningsprocessen. Urvalet är baserat på tidigare statistik framtaget av ESS, som består av ett samarbete mellan 24 europeiska länder. Uppsatsen testar hur olika aspekter av arbetslivet påverkar gränsdragningen mellan arbete och fritid. Resultaten visar att ju högre grad av självbestämmande individen har i sitt arbete, desto mindre missnöjd är hon med tidsfördelning mellan arbete och fritid samt att en högre grad av självbestämmande även tenderar att skapa en oro hos individen på hennes fritid över arbetsrelaterade problem. Vår slutsats är att det finns goda skäl att vara uppmärksam på vad detta alltmer vanliga arbetssätt kan ställa till med för individen. / The purpose of this paper is to explore how different aspects of working life affect the individual. Our starting point in this paper is that we are critical of jobs with high autonomy that is becoming increasingly common. To assist us, we have a lot of previous research as well as critical theory and Foucault’s theory about power and surveillance that guides us through the research process. The selection is based on previous statistics produced by the ESS, which consists of a collaboration between 24 European countries. The essay test different aspects of paid work and its affect on work-life balance. The results show that the higher the degree of autonomy the individual has in his/her work, the less dissatisfied he/she is with time spent between work and leisure. A higher degree of autonomy does in fact lead to the individual worrying about work-related problems in his/her leisure time. Our conclusion is that there are good reasons to pay attention to what this increasingly common tendency can affect the individual.

The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for the risk and prognosis factors. The study results showed that some risk and prognosis factors, such as the demographic factors (race and age), social and family factor (marital status), biomedical factors (tumor size, disease stage, tumor markers and tumor cell differentiation level etc.) and type of treatment patients received had significant effects on survival of the female invasive breast cancer patients.

Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustness in quantile regression through an application of Scott's L 2 Estimation (2001). Theoretic and asymptotic results are presented and used to estimate properties of our method. Along with simple linear regression, semiparametric extensions are examined. To verify our method and its extensions, simulated results are considered. Real data sets are also considered, including estimating the effect of various factors on the conditional quantiles of child birth weight, using semiparametric quantile regression to analyze the relationship between age and personal income, and assessing the value distributions of Major League Baseball players.

Kvicksilverutsläppen har på senare år minskat i omfattning men utsläpp sker fortfarande och det finns ett stort lager i naturen. Oorganiskt kvicksilver är relativt ofarligt för levande organismer, men i akvatiska miljöer kan sulfatreducerande bakterier under syrefria förhållanden omvandla oorganiskt kvicksilver till den betydligt mer skadliga formen metylkvicksilver, meHg, som även är mer biotillgängligt än oorganiskt kvicksilver. Kvicksilver biomagnifieras uppåt i trofinivåerna, från att vara lägst halter i plankton och bottenfauna till att vara högst halter i rovfiskar. Kvicksilver ackumuleras även i fiskars muskelvävnad vilket innebär att yngre fiskar har lägre Hg-halter än gamla och stora individer. På grund av detta finns kostråd framtagna av Livsmedelsverket. Saluförd abborre, liksom ett flertal andra fiskarter, får inneha maximalt 0,5 mg Hg/kg fiskmuskel (färskvikt) och för gädda är gränsen satt till 1 mg Hg/kg fiskmuskel (färskvikt). Organiskt kvicksilver kan påverka foster negativt, och därför bör kvinnor i fertil ålder i största möjliga mån undvika kvicksilverrik fisk.   Examensarbetet syftar till att belysa kvicksilversituationen i Stockholms skärgård för stationär kustfisk. Information har samlats in dels genom en litteraturstudie och dels genom fältundersökningar utförda av IVL Svenska Miljöinstitutet. Genom statistisk analys och stegvis multipel regression visades att kvicksilverhalten i sediment, vattnets klorofyllhalt och områdets vattenvolym bidrog med förklaringsgrad till halten Hg i fisk. Utifrån de mest signifikanta förklarande variablerna togs en statistisk modell fram i syfte att kunna prediktera teoretiska kvicksilverhalter i fisk. Olika modellkombinationer testades och utgående från en modellvalidering valdes den bästa modellen ut. Dess förklaringsgrad är 81 % där kvicksilverhalten i sediment är modellens enda variabel.   Kvicksilversituationen i Stockholms skärgårds kustområden visades grafiskt med hjälp av interpolerade GIS-kartor. Samma områdesavgränsningar användes som i SMHI:s havsområderegister. Det visades att situationen över lag är relativt god, med undantag för de centrala delarna av Stockholm och på så vis kan Stockholm ses som en förorenande punktkälla. / Mercury emissions have been reduced in recent years but the discharges still exist and there exist a great mercury stock out in the nature. Inorganic mercury is relatively harmless for living organisms but during shortage of oxygen, sulphur reducing bacteria can transform inorganic mercury into the considerable more toxic substance methyl mercury, meHg, that are more bio available than inorganic mercury. Mercury biomagnify upwards the trophic levels, where the lowest content exists in algae and sediment living fauna and the highest content exist in predator fishes. Mercury also accumulates in fishes muscle tissue, meaning that young fishes have lower mercury content than old and big individuals. Food advices from the Swedish food agency exist because of this. Perch offered for sale are allowed to have a content of maximum 0.5 mg Hg/kg fish muscle (wet weight) and for pike the upper boundary content is 1 mg Hg/kg fish muscle (wet weight). Inorganic mercury can affect foetus negatively, and therefore women in fertile age should avoid fish with high mercury content for as long as possible.   The aim of this exam work is to illustrate the mercury situation in the archipelago of Stockholm regarding stationary coast fish. Information was collected through a literature study and by field examines done by IVL Swedish Environmental Research Institute Ltd. Through statistical analyses and stepwise multiple regression it was shown that the mercury content in sediment, chlorophyll and the water volume of the area contributed by statistical explanation to the mercury concentration in fish. On the basis of the most significant variables a statistical model was created, with the purpose of predicting theoretical mercury contents in fish. Different model combinations were tested and by a model validation the best model were chosen. Its statistical explanation is 81 % were the mercury content in the sediment is the only input variable to the model.   The mercury situation in the Stockholm archipelago was presented graphically by interpolated maps created with GIS. The same area boundary as SMHI’s maritime registry was used. It was shown that the situation in general is relatively good, with the exception of the central parts of Stockholm. Because of this Stockholm can be seen as a polluting point source.

Denna uppsats behandlar de relativa prisökningar som skett för snusprodukter i Sverige och vill undersöka om dessa har lett till minskad efterfrågan som nationalekonomisk teori föreslår. Utifrån teori och tidigare forskning har en efterfrågemodell konstruerats för att möjliggöra en statistik undersökning. Variablerna som ingår i modellen är inhämtade från Statistiska centralbyråns prisenhet och Swedish Match AB och inkluderar prisuppgifter för varor, försäljningsstatistik och disponibel inkomst under tidsperioden 1999-2009. Resultaten från regressionsanalyser för tidsseriedata visar på att de ökade priserna har haft en negativ inverkan på efterfrågan på snus under den gällande tidsperioden, men att denna effekt varit förhållandevis liten.

In this thesis, we address the identification of biomarkers in high-dimensional omics data. The identification of valid biomarkers is especially relevant for personalized medicine that depends on accurate prediction rules. Moreover, biomarkers elucidate the provenance of disease, or molecular changes related to disease. From a statistical point of view the identification of biomarkers is best cast as variable selection. In particular, we refer to variables as the molecular attributes under investigation, e.g. genes, genetic variation, or metabolites; and we refer to observations as the specific samples whose attributes we investigate, e.g. patients and controls. Variable selection in high-dimensional omics data is a complicated challenge due to the characteristic structure of omics data. For one, omics data is high-dimensional, comprising cellular information in unprecedented details. Moreover, there is an intricate correlation structure among the variables due to e.g internal cellular regulation, or external, latent factors. Variable selection for uncorrelated data is well established. In contrast, there is no consensus on how to approach variable selection under correlation. Here, we introduce a multivariate framework for variable selection that explicitly accounts for the correlation among markers. In particular, we present two novel quantities for variable importance: the correlation-adjusted t (CAT) score for classification, and the correlation-adjusted (marginal) correlation (CAR) score for regression. The CAT score is defined as the Mahalanobis-decorrelated t-score vector, and the CAR score as the Mahalanobis-decorrelated correlation between the predictor variables and the outcome. We derive the CAT and CAR score from a predictive point of view in linear discriminant analysis and regression; both quantities assess the weight of a decorrelated and standardized variable on the prediction rule. Furthermore, we discuss properties of both scores and relations to established quantities. Above all, the CAT score decomposes Hotelling’s T 2 and the CAR score the proportion of variance explained. Notably, the decomposition of total variance into explained and unexplained variance in the linear model can be rewritten in terms of CAR scores. To render our approach applicable on high-dimensional omics data we devise an efficient algorithm for shrinkage estimates of the CAT and CAR score. Subsequently, we conduct extensive simulation studies to investigate the performance of our novel approaches in ranking and prediction under correlation. Here, CAT and CAR scores consistently improve over marginal approaches in terms of more true positives selected and a lower model error. Finally, we illustrate the application of CAT and CAR score on real omics data. In particular, we analyze genomics, transcriptomics, and metabolomics data. We ascertain that CAT and CAR score are competitive or outperform state of the art techniques in terms of true positives detected and prediction error.

Consider the D-optimal designs for a combined polynomial of degree d and trigonometric of order m regression on a partial circle [see Graybill (1976), p. 324]. It is shown that the structure of the optimal design depends only on the length of the design interval and that the support points are analytic functions of this parameter. Moreover, the Taylor expansion of the optimal support points can be determined efficiently by a recursive procedure.

Under path dependent consideration, we discuss vulnerable option pricing problem. Two pricing models are proposed: Model(1) use stepwise regression and Monte Carlo simulation, and Model(2) is based on multi-level regression method. Since the option price was approximated by quadratic surface at each time point in Model(1), large mean square errors are induced. Therefore, we further propose a stepwise subset regression method to improve Model(1) approach. At present, this proposed method can compute the option price accurately for no credit risk options. For Model(2), we utilize a multi-level regression method to price vulnerable options, and simulation results show that the method can also obtain accurate option prices.

Robust regression is a commonly required approach in industrial studies like data mining, quality control and improvement, and finance areas. Among the robust regression methods / Least Median Squares, Least Trimmed Squares, Mregression, MM-method, Least Absolute Deviations, Locally Weighted Scatter Plot Smoothing and Multivariate Adaptive Regression Splines are compared under contaminated normal distributions with each other and Ordinary Least Squares with respect to the multiple outlier detection performance measures. In this comparison / a simulation study is performed by changing some of the parameters such as outlier density, outlier locations in the x-axis, sample size and number of independent variables. In the comparison of the methods, multiple outlier detection is carried out with respect to the performance measures detection capability, false alarm rate and improved mean square error and ratio of improved mean square error. As a result of this simulation study, the three most competitive methods are compared on an industrial data set with respect to the coefficient of multiple determination and mean square error.

ABSTRACT PREDICTION OF LITIGATION PROBABILITY FOR INTERNATIONAL CONSTRUCTION PROJECTS DURING BIDDING STAGE Ayten, ilkay M.S., Department of Civil Engineering Supervisor: Assoc. Prof. Dr. Rifat S&ouml / nmez February 2010, 102 pages Over the years many researchers agreed that between the parties involved in construction projects such as / owner, contractor, engineer and suppliers trying to perform different scopes in different timetables. Therefore, disputes are inevitable due to the complexity of the work. Occurrence of litigation is the most terrifying process to deal with during any construction project for both owner and the contractor because of the time and money consuming nature of the process. Hence, contractors should try to eliminate any potential risk factors that will lead to litigation. The aim of this study is to investigate the factors that influence court action between parties in international construction projects and develop a statistical model that will predict the litigation probability of an international construction project during bidding stage. The final prediction model revealed that contractual awareness and consciousness of risk factors is the key to predict litigation probability. Considering awareness of the factors affecting litigation probability are displayed in this thesis. Companies may have the opportunity to develop risk assessment and management strategies while reconsidering their contingency estimates.