Generating random variates as generalisation of a given sample is an important task for stochastic simulations. The three main methods suggested in the literature are: fitting a standard distribution, constructing an empirical distribution that approximates the cumulative distribution function and generating variates from the kernel density estimate of the data. The last method is practically unknown in the simulation literature although it is as simple as the other two methods. The comparison of the theoretical performance of the methods and the results of three small simulation studies show that a variance corrected version of kernel density estimation performs best and should be used for generating variates directly from a sample. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

The aim of this thesis is to analyze the properties of robust estimates and to compare these estimates with regard to the properties between them. The analysis of properties depends on the type of a variable (continuous or discrete), its probability distribution, the range of random sample and the proportion of outliers in random sample. Comparing the properties in different situations will give "guidance" to determine which of the estimates is preferable in specific situations, and which of them should be rather avoided. An adjusted bootstrap method is used to obtain the estimates of properties estimates. The thesis is devided into two parts. In the first part, the parameter estimates, type and design of robust estimators and the bootstrap method are monitored. In the second practical part we determine the suitability of bootstrap to obtain estimates of the properties of robust estimators, followed by obtaining estimates of the properties of the estimates and compare them. At the conclusion of the practical part we observe and compare the values of bootstrap confidence intervals on real data on household income. The results of this thesis shows us, that the bootstrap method does not provide good estimates of the properties of robust estimators in all cases. The results also bring us to the conclusion that from a certain extent of random sample regardless of the number of outliers, you can choose from a robust estimate only on the basis of its value, properties of robust estimates are very similar. Contemplated robust estimates of variability are not suitable estimates in most cases.

We develop and evaluate algorithms for generating random variates for simulation input. One group called automatic, or black-box algorithms can be used to sample from distributions with known density. They are based on the rejection principle. The hat function is generated automatically in a setup step using the idea of transformed density rejection. There the density is transformed into a concave function and the minimum of several tangents is used to construct the hat function. The resulting algorithms are not too complicated and are quite fast. The principle is also applicable to random vectors. A second group of algorithms is presented that generate random variates directly from a given sample by implicitly estimating the unknown distribution. The best of these algorithms are based on the idea of naive resampling plus added noise. These algorithms can be interpreted as sampling from the kernel density estimates. This method can be also applied to random vectors. There it can be interpreted as a mixture of naive resampling and sampling from the multi-normal distribution that has the same covariance matrix as the data. The algorithms described in this paper have been implemented in ANSI C in a library called UNURAN which is available via anonymous ftp. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

[pt] Nos últimos anos, o Brasil tem enfrentado frequentes desafios para garantir o suprimento de energia elétrica. A produção de energia fotovoltaica tem ganhado destaque, pois a fonte solar é um recurso abundante e renovável. No entanto, no âmbito do planejamento da operação energética de médio prazo, a estimativa proveniente dos parques geradores fotovoltaicos tem caráter determinístico. Quanto à geração fotovoltaica distribuída, pouco se sabe como é feita sua consideração para os próximos anos. Nesse contexto, essa dissertação propõe uma metodologia para mensuração do impacto do potencial de geração fotovoltaica residencial no planejamento da operação energética de médio prazo. Para alcançar tal objetivo, utilizou-se a metodologia Box and Jenkins com simulação de cenários via Bootstrap, levando em consideração os níveis de irradiação solar, a área de telhado útil e a eficiência na conversão do recurso solar em eletricidade. Os principais resultados dessa dissertação são: custo total da operação, custo marginal de operação, energia armazenada, custo de déficit, risco de déficit, geração hidráulica, geração térmica, intercâmbio de energia e custo de vertimento. A consideração de cenários do potencial de geração fotovoltaica residencial reduziu o custo total da operação nos Programas Mensais da Operação Energética de janeiro/2015 e janeiro/2016, chegando a máxima redução de (7,8 por cento) e (1,5 por cento), respectivamente. Os demais resultados também foram impactados. Conclui-se que, a geração fotovoltaica residencial impacta, significativamente, o planejamento da operação energética de médio prazo, sendo necessário o desenvolvimento de outros estudos para avaliar a inserção e evolução dessa geração na matriz energética brasileira. / [en] In recent years, Brazil has faced frequent challenges to ensure the supply of electricity. The production of photovoltaic energy has gained prominence, because the solar source is an abundant and renewable resource. However, in the context of long-term energy operation planning, the estimation from photovoltaic generating parks is deterministic. As for distributed photovoltaic generation, little is known how its consideration is made for the next few years. In this context, this Master s thesis proposes a methodology for measuring the impact of the potential of residential photovoltaic generation in the planning of medium-term energy operation. In order to achieve this objective, the Box and Jenkins methodology was simulated using Bootstrap scenarios, taking into account the levels of solar radiation, the useful roof area and the efficiency in the conversion of the solar resource into electricity. The main results of this dissertation are: total cost of operation, marginal cost of operation, stored energy, cost of deficit, deficit risk, hydraulic generation, thermal generation, energy exchange and delivery cost. The consideration of scenarios of the potential of residential photovoltaic generation reduced the total cost of operation in the Monthly Energy Operation Programs of January 2015 and January 2016, reaching the maximum reduction of 7.8 percent and 1.5 percent), Respectively. The other results were also impacted. It is concluded that, residential photovoltaic generation significantly impacts medium-term energy operation planning, and it is necessary to develop other studies to evaluate the insertion and evolution of this generation in the Brazilian energy matrix.