Denna studie genomfördes 25 mars till 27 maj 2013 på C-nivå vid Södertörns Högskola.  I studien studeras tre svenska kvinnor och fyra svenska män som väljer att inte ha barn. De övergripande frågeställningarna är: Hur beskriver kvinnor och män sitt val att inte ha barn?Hur bemöts de som väljer att inte ha barn av omgivningen? Hur svarar de som väljer att inte ha barn på bemötandet från omgivningen? Studien är kvalitativ där grundliga personliga intervjuer genomfördes. För analys av intervjumaterial valdes teorier om heteronormativitet, stämpling samt stigmatisering. Studiens resultat visar att kvinnor och män som väljer att inte ha barn, i olika grad beroende på sammanhang, blir ifrågasatta och får möta negativa reaktioner från omgivningen. Det är under en viss tidsperiod i livet som pressen från omgivningen på att ha barn är som starkast. Personer som väljer att inte ha barn blir positionerade som avvikande i samhället samtidigt som deras avvikande position osynliggörs då de inte tas på allvar och anses av omgivningen vara öppna för övertalningsförsök. Nämnvärd skillnad mellan kvinnor och män har inte kunnat urskiljas efter analys av studiens intervjumaterial.

After the 1999 Marmara Earthquake, social, economic and engineering studies on earthquakes became more intensive. The Turkish Catastrophe Insurance Pool (TCIP) was established after the Marmara Earthquake to share the deficit in the budget of the Government. The TCIP has become a data source for researchers, consisting of variables such as number of claims, claim amount and magnitude. In this thesis, the TCIP earthquake claims, collected between 2000 and 2008, are studied. The number of claims and claim payments (aggregate claim amount) are modeled by using Generalized Linear Models (GLM). Observed sudden jumps in claim data are represented by using the exponential kernel function. Model parameters are estimated by using the Maximum Likelihood Estimation (MLE). The results can be used as recommendation in the computation of expected value of the aggregate claim amounts and the premiums of the TCIP.

Det övergripande syftet med denna uppsats är att försöka förstå hur unga kvinnor upplever att ha ett digitalt jag på den sociala medieplattformen Facebook. För att få en förståelse av deras upplevelse har jag valt att dela upp teoriavsnittet i två större delar. Den första delen syftar på att förklara konsekvenserna av att leva i en värld som präglas av medier. Utgångspunkten ligger i McLuhans välkända begrepp ”The medium is the message” (1964/2001) för att förstå hur de nya sociala medierna blir en förlängning av den egna fysiska kroppen och Thompsons interaktionstyper (1995/2001). Det andra teoriavsnittet behandlar hur man kan försöka förstå människors upplevelse av att leva i en värld som präglas av medier. Här har jag valt att utgå från Sherry Turkles teorier (1995/1997) om hur Internet har blivit en plats där människor kan laborera med olika konstruktioner av jaget. Jag har valt att komplettera detta med Jenny Sundéns teorier i hennes avhandling Material virtualities (2002) samt med fenomenologin som avser att studera upplevelsen av ett fenomen. Den fenomenologiska metoden använder sig av kvalitativa undersökningar och jag har i min studie utfört fem kvalitativa, semistrukturerade intervjuer med unga kvinnor mellan 24-30 år. Resultatet av undersökningen visar att unga kvinnor upplever sitt digitala jag som en förlängning av det verkliga jaget, men även begränsat då Facebook endast återger en begränsad bild av jaget. Det fanns även en medvetenhet kring att de framställde det egna jaget positivt genom att de var selektiva i vilken information de publicerade om sig själva. Detta påverkade också tilltron till andras digitala jag. Vidare upplevde kvinnorna att de till viss mån var begränsade i kommunikationen på Facebook i relation till kommunikationen ansikte mot ansikte. Facebook upplevdes dels som ett fysiskt rum där de kunde vara (med sina sinnen). Det framkom också att kvinnorna upplevde att detta rum hade en egen tid som fortlöpte även då de själva inte var där.

In this study two different hybrid ranking approaches based on data envelopment analysis and outranking relations for ranking alternatives are proposed. Outranking relations are widely used in Multicriteria Decision Making (MCDM) for ranking the alternatives and appropriate in situations when we have limited information on the preference structure of the decision maker. Yet to apply these methods DM should provide exact values for method parameters (weights, thresholds etc.) as well as basic information such as alternative scores. DEA is used for classification of decision making units according to their efficiency scores in a non-parameteric way. The proposed hybrid approaches utilize PROMETHEE (a well known method based on outranking relations) to construct outranking relations by pairwise comparisons and a technique similar to DEA crossefficiency ranking for aggregating comparisons. While first of the proposed approaches can deal with imprecise specification of criterion weights, second approach can utilize imprecise weights and thresholds.

This study aimed to reveal the relationship between quality of life and happiness in a comparative manner, to underline the importance of happiness and enable researchers to familiarize themselves with happiness in the sociological context of Turkey. Quality of life and happiness, which are multidimensional and interdisciplinary concepts, were firstly identified by looking at their treatment in different approaches. Then, they were analyzed by relating them to different variables in the European Quality of Life Survey (EQLS) data which allows for making comparisons among European Union countries and Turkey. Our study is limited to four countries, namely Bulgaria, Denmark, Hungary and Turkey that were selected on the basis of two criteria / their average of happiness score and their membership status in the European Union. On the other hand, the independent variables of the study are &ldquo / having&rdquo / (material living conditions), &ldquo / loving&rdquo / (social relations), &ldquo / being&rdquo / (quality of society), &ldquo / time pressure&rdquo / (work-life balance), &ldquo / alienation&rdquo / , &ldquo / environment&rdquo / , internet using, health and socio-demographic variables namely gender, age, region, employment status and educational level. Consequently, improving quality of life resulted in happiness. In other words, the main aim of improving quality of life is to supply, improve and increase happiness.

In this thesis, pairwise multiple comparisons and multiple comparisons with a control are studied when the observations have short-tailed symmetric distributions. Under non-normality, the testing procedure is given and Huber estimators, trimmed mean with winsorized standard deviation, modified maximum likelihood estimators and ordinary sample mean and sample variance used in this procedure are reviewed. Finally, robustness properties of the stated estimators are compared with each other and it is shown that the test based on the modified maximum likelihood estimators has better robustness properties under short-tailed symmetric distribution.

In statistical analysis, distributional assumptions are needed to apply parametric procedures. Assumptions about underlying distribution should be true for accurate statistical inferences. Goodness-of-fit tests are used for checking the validity of the distributional assumptions. To apply some of the goodness-of-fit tests, the unknown population parameters are estimated. The null distributions of test statistics become complicated or depend on the unknown parameters if population parameters are replaced by their estimators. This will restrict the use of the test. Goodness-of-fit statistics which are invariant to parameters can be used if the distribution under null hypothesis is a location-scale distribution. For location and scale invariant goodness-of-fit tests, there is no need to estimate the unknown population parameters. However, approximations are used in some of those tests. Different types of estimation and approximation techniques are used in this study to compute goodness-of-fit statistics for complete and censored samples from univariate distributions as well as complete samples from bivariate normal distribution. Simulated power properties of the goodness-of-fit tests against a broad range of skew and symmetric alternative distributions are examined to identify the estimation effects in goodness-of-fit tests. The main aim of this thesis is to modify goodness-of-fit tests by using different estimators or approximation techniques, and finally see the effect of estimation on the power of these tests.

Microarray technology allows the simultaneous measurement of thousands of gene expressions simultaneously. As a result of this, many statistical methods emerged for identifying differentially expressed genes. Kerr et al. (2001) proposed analysis of variance (ANOVA) procedure for the analysis of gene expression data. Their estimators are based on the assumption of normality, however the parameter estimates and residuals from this analysis are notably heavier-tailed than normal as they commented. Since non-normality complicates the data analysis and results in inefficient estimators, it is very important to develop statistical procedures which are efficient and robust. For this reason, in this work, we use Modified Maximum Likelihood (MML) and Adaptive Maximum Likelihood estimation method (Tiku and Suresh, 1992) and show that MML and AMML estimators are more efficient and robust. In our study we compared MML and AMML method with widely used statistical analysis methods via simulations and real microarray data sets.

Modeling and forecasting of the unemployment rate of a country is very important to be able to take precautions on the governmental policies. The available unemployment rate data of Turkey provided by the Turkish Statistical Institute (TURKSTAT) are not in suitable format to have a time series model. The unemployment rate data between 1988 and 2009 create a problem of building a reliable time series model due to the insufficient number and irregular form of observations. The application of disaggregation methods to some parts of the unemployment rate data enables us to fit an appropriate time series model and to have forecasts as a result of the suggested model.

Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.