This thesis discusses about possibilities of construction colour DNA spectrograms and about patterns which can be detected there. Spectrograms as tools of spectral analysis give us a simultaneous view of the local frequency throughout the nucleotide sequence. They are suitable for gene identification or gene regions identification, determination of global character about whole chromosomes and also give us a chance for the discovery of yet unknown regions of potential significance. For purpose of this kind of DNA analysis is possible to use digital signal processing methods. We can apply them on only after conversion of DNA sequence to numerical representation. Selection of correct numerical representation affects how well will be reflected biological features in numerical record which we need for another use in digital signal analysis.

This dissertation examines a number of issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged based on its usefulness for estimation and testing and its ability to model the heteroskedastic conditional variance. It is also compared with the traditional Normal Linear Regression Model. Subsequently the analysis is broadened to a dynamic setup. The Student t Autoregressive Model is derived and a number of its operational forms are considered. Three forms are selected for a detailed examination in a series of Monte Carlo experiments. The models’ usefulness for estimation and testing is evaluated, as well as their ability to model the conditional variance. The models are also compared with the traditional Dynamic Linear Regression Model. / Ph. D.

In this study, we extend the spatial weight matrix defined by Getis and Aldstadt (2004) to a more general case. The modified spatial weight matrix performs better than the original spatial weight matrix since the modified spatial weight matrix adjusts weights of observations based on the distance from other observations. Both the simulation study and the application to the ecological process of invasion of non-native invasive plants (NNIPs) provide evidences for the better performance of the modified spatial weight matrix. We also develop procedures that can be used to quantify the invasion stages of NNIPs. The resultant map of invasion stage on county-level provides a useful and meaningful tool for policy makers; especially, it can be used to optimize allocation of management resources. The result of simultaneous autoregressive model shows that not only the biotic and abiotic factors but also human activities play an important role in the establishment and spread of multiflora rose in the Upper Midwest. It also shows the tendency of the establishment and spread of multiflora rose (Rosa Multiflora, Thunb. ex Murr.) in the Upper Midwest.

The objective of this study is to develop a market sentiment model for financial markets using machine learning, and to illustrate these methods using commodity price data. A market sentiment model may capture the fundamental and crowd psychology of the market, through a variable that uses positive and negative words and phrases. The commodity price used is the daily price of the spot crude oil exchange-traded fund (ETF), United States Oil Fund (USO). The forecasting power of the market sentiment model is compared with a traditional autoregressive model. The results showed that the autoregressive models did not have significant forecasting power for the oil data over the time period examined and the addition of the sentiment model did not improve the forecasting power. Machine learning is a relatively new forecasting method. Therefore, further research on this topic is needed before any firm conclusions can be drawn regarding the effectiveness of this approach. / February 2017

My thesis will focus on optimal investment decisions, especially those that are planned for longer investment horizon. I will review the literature, showing that changes in investment opportunities can alter the risk-return tradeoff over time and that asset return predictability has an important effect on the variance and correlation structure of returns on bonds, stocks and T bills across investment horizons. The main attention will be given to pension funds, which are institutional investors with relatively long investment horizon. I will find the term structure of risk-return tradeoff in the empirical part of this paper. Later on I will add some variables into the model and investigate whether it can improve the results. Finally the optimal investment strategies will be constructed for various levels of risk tolerance and the results will be compared with strategies of Czech pension funds. I am going to use data from Thomson Reuters Datastream, Wharton Research Data Services and additionally from some other sources.

This work investigates mainly panel data models in which cross-sections can be considered independent. In the first part, we summarize results in the field of pool models and one-way error component models with fixed and random effects. We focus especially on the ways of estimating unknown parameters and on effects significance tests. We also briefly describe two-way error component model issues. In the second part, estimators of first order autoregressive panel data model parameters are derived, for both fixed and random parameters case. The work proves unbiasedness, consistency and asymptotic normality of selected estimators. Using these features, hypothesis tests about corresponding parameters are derived. Application of models is illustrated using real data and simulated data examples. Powered by TCPDF (www.tcpdf.org)

Airbus réalise pour chaque avion et pour chaque équipement de nombreux essais, au sol ou en vol et doit garantir qu'en tout point de vol possible, la température de chacun des équipements reste inférieure à la température limite correspondante. Pour pouvoir valider la température de chaque équipement dans l'enveloppe de vol, il faudrait disposer d'essais réalisés aux frontières. Or, tous les essais en vol sont confrontés aux contraintes climatiques et opérationnelles qui ne permettent pas d'explorer tout le domaine. C'est pourquoi Airbus a besoin d'élaborer des méthodes d'extrapolation de température, de manière à prédire le comportement thermique des matériaux et des équipements dans les pires conditions. Les techniques proposées sont basées sur la théorie de l'identification de systèmes qui consiste à déterminer des modèles de comportement d'un point de vue heuristique à partir de mesures et considérations physiques. Plus précisément, le présent document valide les modèles ARX comme un outil pour l'identification de la température du système. Les modèles et les techniques sont étudiés, tout d'abord, d'un point de vue de la simulation numérique et après, confrontés face à des tests représentatifs au laboratoire. Les techniques proposées permettent prédire la température des composants avion pour des conditions différentes / Airbus must ensure that for all flight conditions that a given aircraft could face, the temperature of each powerplant system must be less than the corresponding critical temperature. In order to validate the temperature of each device in the flight envelope, tests at the border should be done. Airbus produces for each aircraft component many trials, either in flight or ground. However, all flight tests are faced with climatic and operational constraints which do not permit exploring the whole area. That's why Airbus needs to develop methods of extrapolation of temperature in order to predict the thermal behavior of materials and equipments in the worst conditions. The proposed techniques are based on the system identification theory which consists on heuristically determining an analytical model using physical insights and measurements. More precisely, this paper validates ARX models as a tool for the identification of the system's temperature. The models and techniques are studied, first, from a numerical simulation point of view and second, based on laboratory representative tests. The proposed techniques allow predicting the temperature of aircraft components at different conditions

This thesis focuses on estimating parameters of appropriate model for daily returns using the Markov Chain Monte Carlo method (MCMC) and Bayesian statistics. We describe MCMC methods, such as Gibbs sampling and Metropolis- Hastings algorithm and their basic properties. After that, we introduce different financial models. Particularly we focus on the lognormal autoregressive model. Later we theoretically apply Gibbs sampling to lognormal autoregressive model using principles of Bayesian statistics. Afterwards, we analyze procedu- res, that we used in simulations of posterior distribution using Gibbs sampling. Finally, we present processed output of both simulated and real data analysis.

[pt] Durante as últimas duas décadas, as economias do mundo têm sido caracterizadas por maior estabilidade na inflação e no produto. No entanto, aumentos na instabilidade financeira vêm preocupando os bancos centrais. Sendo assim, este trabalho tem como objetivo analisar empiricamente possíveis relações entre a política monetária brasileira e variações em preços de ativos, mais especificamente taxa de câmbio nominal e índice Bovespa. Os resultados encontrados para o período amostral de janeiro/2000 a janeiro/2006 sugerem que o modelo não-linear (TAR - threshold autoregressive) ajustase melhor aos dados brasileiros em comparação com o modelo linear e trazem indícios de que variações na taxa de câmbio nominal estão associadas a movimentos na taxa Selic em períodos mais conturbados da economia brasileira, os quais geram maior volatilidade no mercado financeiro. / [en] During the past two decades, world's economies have been characterized by stability on inflation and product levels. However, increases in financial instability are becoming a reason for concern to central banks. In this sense, the aim of this study is to analyze empirically possible relations between the Brazilian monetary policy and changes on asset prices, specifically the nominal exchange rate and the Bovespa index. The results for the sample between January, 2000 and January, 2006 suggest that the non-linear model, based on a Threshold Autoregressive model, fits better into Brazilian data than the linear model and find evidence that changes on nominal exchange rates and movements on Selic rate are associated during difficult times of the Brazilian economy, which are related to higher financial volatility.

Os sinais de eletromiografia de agulha podem ser modelados por um sistema linear invariante no tempo (SLIT). A pergunta é: Quantos coeficientes são necessários para tal? O presente mestrado estuda, para sinais de eletromiografia de agulha gravados sob as mesmas condições experimentais, como varia o número ótimo de coeficientes autorregressivos com o comprimento das épocas e com a força de contração muscular concomitantemente. O estudo foi realizado tendo como base sinais de 10%, 25%, 50% e 80% da máxima contração voluntária (MCV) e tendo épocas de 500ms, 250ms, 100ms, 50ms e 25ms de seis indivíduos normais. Desta forma, uma função densidade de probabilidade é sugerida para a ordem do modelo autorregressivo que melhor descreva o sinal de eletromiografia obtido a uma força de contração específica e que tenha uma duração de época definida. / Needle electromyography signals (EMG) can be modeled by a linear time invariant system (LTI). The posed question is How many coefficients are needed for an adequate modeling? This Masters dissertation studies how the optimal number of autoregressive coefficients changes concomitantly with the epoch length and the muscle contraction force for needle electromyography signals recorded under the same experimental conditions. The study was conducted on signals from six normal individuals at 10%, 25%, 50% and 80% of the maximum voluntary contraction and epoch lengths of 500ms, 250ms, 100ms, 50ms and 25ms. Thus, a probability density function is suggested for the autoregressive model order that best describes the electromyographic signal obtained at a specific \"contraction force\" and has a defined \"epoch length\".