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Vi har genomfört en enkätundersökning om restaurangers och serveringars ekonomiska utsatthet och anpassning under coronapandemin. 139 restauranger och serveringar från Stockholms kommun valdes slumpmässigt ut för att delta i studien. Varje servering fick tillgång till ett formulär, som bland annat inkluderade frågor relaterade till beställnings- och överlämningsmetoder samt försäljning. Svaren från respondenterna användes sedan för att göra punkt- och intervallskattningar om olika parametrar i populationen. Vi tar även fram en logistisk regressionsmodell för att skatta sannolikheter för att serveringar genomför olika anpassningsmetoder. Utifrån urvalet kan vi se att många serveringar har valt att införa någon sorts ny strategi som svar på pandemin. För de flesta har dock försäljningen minskat. Det verkar också som att serveringar som har fått färre kunder tenderar att ha en högre sannolikhet att anpassa sig. På grund av att bara runt 15,83 procent av de kontaktade serveringarna valde att svara, finns det stor osäkerhet i våra skattningar. / We have conducted a survey about the adaptation and economic vulnerability of restaurants and food services during the COVID-19 pandemic. 139 restaurants from Stockholm Municipality were randomly chosen to participate. Each restaurant was provided with a form that included questions related to different methods for ordering and serving food, and sales. The answers from the respondents were later used to form point and interval estimates for different population parameters. We have also used a logistic regression model to estimate the probability that food services utilize a new adaptation strategy. The results from the sample indicate that many food services have chosen some kind of new strategy in response to the pandemic. For most, sales have decreased. It also seems to be the case that food services that have had a decrease in customers tend to have higher probability for adaptation. Since only 15.83 percent of the food services that were included in the sample chose to participate, there exists large uncertainty in our estimates.

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Syftet med denna uppsats var att med hjälp av Markovkedjor göra en statistisk prognos för ensnöfylld jul för tre svenska väderstationer. Datamängderna som ligger till grund förskattningarna av övergångssanolikheterna sorterades så endast perioden 17 december – 31december för varje år användes. Fundamentet för prognosen bygger på den underliggandeteorin, framförallt av den så kallade Markovegenskapen och Chapman-Kolmogorovsekvation. Prognosen har två utgångspunkter: antingen är det snö den 17 december eller inte.Uppsatsen undersöker också kedjornas ergodicitet och kedjornas stationära fördelningar -experimentellt, och med en teoretisk lösning. Sannolikheten för snö på julafton ansågs somgoda, förutsatt att det är snö den 17 dec. Att tillämpa Markovkedjor för denna sortens problemfungerade bra, vilket beror på det beroende som finns mellan dagens väder och vädretimorgon. / The purpose of this paper was to make a statistical forecast for a snow-filled Christmas forthree Swedish weather stations with the help of Markov chains. The data on which thetransition probabilities estimates were based on, were sorted so that only the period December17 – December 31 for each year was used. The basis for the forecast is based on theunderlying theory, the so-called Markov property and the Chapman-Kolmogorov equation.The forecast has two starting points: whether it is snow on December 17 or not. The thesisalso examined the ergodicity and the stationary distributions of the Markov chains –experimentally and with a theoretical solution. The probability of snow on Christmas Eve wasconsidered good, provided there is snow on Dec 17. Applying Markov chains to this kind ofproblem worked well, due to the dependency that exists between today’s weather and theweather tomorrow

This paper investigates which factors best predict the economic state of a Swedish municipality after the 2008 crisis by constructing a linear model that regresses the change in the unemployment rate on a set of variables. The variables used for the model were from a dataset put together using data from a government service and were selected for the model using Bayesian information criterion. From this procedure, a model with six independent variables was estimated. The model’s statistics were examined, and the model was subsequentially tried against the five multiple linear regression assumptions. It was concluded that the model did not fulfil the assumption of homoscedasticity, and because of this, the dependent variable was transformed into a logarithm, thus yielding a log-lin model. This model ended up fulfilling every assumption and had higher explanatory power than the previous model. It is concluded that the variables that denote the number of newly registered businesses per 1000 residents, the share of residents with a high education, the fraction of net-commuters, the number of refugees received with a residence permit per 1000 residents, total net investments per person, the share of long term unemployed residents and the population size all prove significant when included together in a log-lin model of the change in the unemployment rate.

In this paper, we are going to study the use of opioid substances after undergoing a Hematopoietic Stem Cell transplant in Sweden. The purpose of the study is divided into two parts, where the first objective is to display the use of opioids within the population using descriptive statistics. The second objective is to model the effect of opioid substances on survival using Cox Proportional Hazard regression. From the descriptive part, we can see that women tend to take opioids to a slightly greater extent than men, and that there are great differences among age groups were younger patients tend to withdraw more opioids. We create three different models measuring the effect of opioid use within the 43 first days after transplant on survival, correcting for four potential confounders: sex, age at transplant, source of transplant and relationship to the donor. The first two models are fitted with different measurements of survival time, and the third model is a stratified Cox regression based on model 2. The three models somewhat differ from each other in terms of estimated hazard ratios, however, we cannot show a statistically significant effect of opioid use within the first 43 days on survival in any of the models.

This project focuses on how to implement an Artificial Intelligence (AI) -agent in a Tactical Simulator (Tacsi). Tacsi is a simulator used by Saab AB, one thing that the simulator is used for is pilot training. In this work, Tacsi will be used to simulate air to air combat. The agent uses Reinforcement Learning (RL) to be able to explore and learn how the simulator behaves. This knowledge will then be exploited when the agent tries to beat a computer-controlled synthetic enemy. The result of this study may be used to produce better synthetic enemies for pilot training. The RL-algorithm used in this work is deep Q-Learning, a well-known algorithm in the field. The results of the work show that it is possible to implement an RL-agent in Tacsi which can learn from the environment and be able to defeat the enemy, in some scenarios. The result produced by the algorithm verified that a RL-Agent works within Tacsi at Saab AB. Although the performance of the agent in this work is not impressive, there is a great opportunity for further development of the agent as well as the working environment.

This thesis consists of two papers related to systems of Snell envelopes. The first paper uses a system of Snell envelopes to formulate the problem of two-modes optimal switching for the full balance sheet in finite horizon. This means that the switching problem is formulated in terms of trade-off strategies between expected profit and cost yields, which act as obstacles to each other. Existence of a minimal solution of this system is obtained by using an approximation scheme. Furthermore, the optimal switching strategies are fully characterized. The second paper uses the Snell envelope to formulate the fair price of Bermudan options. To evaluate this formulation of the price, the optimal stopping strategy for such a contract must be estimated. This may be done recursively if some method of estimating conditional expectations is available. The paper focuses on nonparametric estimation of such expectations, by using regularization of a least-squares minimization, with a Tikhonov-type smoothing put on the partial diferential equation which characterizes the underlying price processes. This approach can hence be viewed as a combination of the Monte Carlo method and the PDE method for the estimation of conditional expectations. The estimation method turns out to be robust with regard tothe size of the smoothing parameter. / QC 20111013