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1	Tankekontroll och arbetsminne: Sambandet mellan tankesuppression, ruminering, GABA och proaktiv interferens Petersson, Emma, Silfverberg, Li January 2021 (has links) Arbetsminne och flertalet exekutiva funktioner är avhängiga varandra; en av dessa funktioner är förmågan till tankesuppression, det vill säga att kunna hålla tillbaka tankar som är irrelevanta för sammanhanget. Detta är en viktig förmåga att ha när vi lär in ny information, eftersom tidigare inlärd information kan blandas ihop med ny, vilket gör det svårt för arbetsminnet att sortera bland de olika stimulusen eller den information som aktuellt hålls där. Detta kallas för proaktiv interferens. Syftet med denna studie var att undersöka potentiella samband mellan proaktiv interferens, ruminering, tankesuppression och neurotransmittorn GABA. För datainsamlingen användes beteendetestet 2-back som mäter proaktiv interferens, självskattningsformulären RRS-BR som mäter ruminering och WBSI som mäter tankesuppression samt MR-kamera som mäter GABA. 31 deltagare, både äldre och yngre, rekryterades till denna studie. Korrelationsanalyser kunde påvisa en signifikant medelstark positiv korrelation mellan GABA och svårighet för tankesuppression, samt en signifikant stark positiv korrelation mellan svårighet för tankesuppression och ruminering. Mediationsanalys visade att tankesuppression medierar förhållandet mellan GABA och ruminering. Arbetsminne proaktiv interferens ruminering tankesuppression GABA 2-back-test Psychology Psykologi
2	Konstrukce zařízení pro výzkum mazání ozubených převodů / Design of the device for research on lubrication of gears Žáček, Jan January 2018 (has links) The master´s thesis is focused on the design and implementation of the laboratory stand for studying lubrication of gears. Specifically, to assess the effect of lubricant contamination on gears. The device uses the Niemann closed circuit concept which in practice is called „back to back“. Firstly, in the theoretical part, the test circuits uses for testing gears are described and one of the standardized procedure for experimentation is presented. Based on research studies, basic parameters for the design and creation of conceptuals are determined. The practical part consists not only the design of this stend, but also including test run and the description of the procedure on that device. The result of this master´s thesis is the functional experimental device for the future development of intelligent lubrication systems.
3	利用混合模型估計風險值的探討阮建豐 Unknown Date (has links) 風險值大多是在假設資產報酬為常態分配下計算而得的，但是這個假設與實際的資產報酬分配不一致，因為很多研究者都發現實際的資產報酬分配都有厚尾的現象，也就是極端事件的發生機率遠比常態假設要來的高，因此利用常態假設來計算風險值對於真實損失的衡量不是很恰當。針對這個問題，本論文以歷史模擬法、變異數-共變異數法、混合常態模型來模擬報酬率的分配，並依給定的信賴水準估算出風險值，其中混合常態模型的參數是利用準貝式最大概似估計法及EM演算法來估計；然後利用三種風險值的評量方法：回溯測試、前向測試與二項檢定，來評判三種估算風險值方法的優劣。經由實證結果發現： 1.報酬率分配在左尾臨界機率1％有較明顯厚尾的現象。 2.利用混合常態分配來模擬報酬率分配會比另外兩種方法更能準確的捕捉到左尾臨界機率1％的厚尾。 3.混合常態模型的峰態係數值接近於真實報酬率分配的峰態係數值，因此我們可以確認混合常態模型可以捕捉高峰的現象。關鍵字：風險值、厚尾、歷史模擬法、變異數-共變異教法、混合常態模型、準貝式最大概似估計法、EM演算法、回溯測試、前向測試、高峰 / Initially, Value at Risk (VaR) is calculated by assuming that the underline asset return is normal distribution, but this assumption sometimes does not consist with the actual distribution of asset return. Many researchers have found that the actual distribution of the underline asset return have Fat-Tail, extreme value events, character. So under normal distribution assumption, the VaR value is improper compared with the actual losses. The paper discuss three methods. Historical Simulated method - Variance-Covariance method and Mixture Normal .simulating those asset, return and VaR by given proper confidence level. About the Mixture Normal Distribution, we use both EM algorithm and Quasi-Bayesian MLE calculating its parameters. Finally, we use tree VaR testing methods, Back test、Forward tes and Binomial test -----comparing its VaR loss probability We find the following results: 1.Under 1% left-tail critical probability, asset return distribution has significant Fat-tail character. 2.Using Mixture Normal distribution we can catch more Fat-tail character precisely than the other two methods. 3.The kurtosis of Mixture Normal is close to the actual kurtosis, this means that the Mixture Normal distribution can catch the Leptokurtosis phenomenon. Key words: Value at Risk、VaR、Fat tail、Historical simulation method、 Variance-Covariance method、Mixture Normal distribution、Quasi-Bayesian MLE、EM algorithm、Back test、 Forward test、 Leptokurtosis 風險值厚尾歷史模擬法變異數-共變異數法混合常態模型準貝式最大概似估計法 EM演算法回溯測試前向測試高峰 Value at risk (VaR) Fat tail Historical simulation method Variance-covariance method Mixture normal distribution Quasi-bayesian MLE EM algoritm Back test Forward test Leptokurtosis
4	A Multi-Factor Stock Market Model with Regime-Switches, Student's T Margins, and Copula Dependencies Berberovic, Adnan, Eriksson, Alexander January 2017 (has links) Investors constantly seek information that provides an edge over the market. One of the conventional methods is to find factors which can predict asset returns. In this study we improve the Fama and French Five-Factor model with Regime-Switches, student's t distributions and copula dependencies. We also add price momentum as a sixth factor and add a one-day lag to the factors. The Regime-Switches are obtained from a Hidden Markov Model with conditional Student's t distributions. For the return process we use factor data as input, Student's t distributed residuals, and Student's t copula dependencies. To fit the copulas, we develop a novel approach based on the Expectation-Maximisation algorithm. The results are promising as the quantiles for most of the portfolios show a good fit to the theoretical quantiles. Using a sophisticated Stochastic Programming model, we back-test the predictive power over a 26 year period out-of-sample. Furthermore we analyse the performance of different factors during different market regimes. finance statistics stock market stocks factor factors probability probability distribution students t distrbution students t copula markov chain hidden markov model regime switching stochastic programming optimisation optimization multi factor model arbitrage pricing theory return performance back test expectation maximisation expectation maximization multiple linear regression stochastic process primal-dual interior point qq-plot qq plot excess return market regimes bear market bull market market index index Probability Theory and Statistics Sannolikhetsteori och statistik

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