Main goal of the diploma thesis is to describe household consumption and its survey in the Czech Republic not only from the macroeconomic but mainly from the microeconomic standpoint. Consumption expenditures are the main subject of many long-term analysis and forecasts which help by implementation of economic and politcal precautions. In connection with the historical development of statistic as a scientific disciplin there is also a significant stress on a methodical development of household budget surveys which examine consumption expenditures of different social groups and household types. After the analysis of development and structure of household consumption surveyed by National Accounts and Household Budget Surveys the attention is given also to household consumption in member states of the European Union among which is also the Czech Republic.

Statistically significant prediction of stock price changes requires security returns' correlation with, or dependence upon, some variable(s) across time. Since a security's past return is commonly employed in forecasting, and because the lack of lower-order correlation does not guarantee higher-order independence, nonlinear testing that focuses on higher-order moments of stock return distributions may reveal exploitable stock return dependencies. This dissertation fits AR models to TAQ data sampled at ten-minute intervals for 20 small-capitalization, 20 mid-capitalization, and 20 large-capitalization NYSE securities, for the years 1993, 1995, 1997, 1999 and 2001. The Hinich Patterson Bicovariance statistic (to reveal nonlinear and linear autocorrelation) is computed for each of the 1243 trading days for each of the 60 securities. This statistic is examined to see if it is more or less likely to occur in securities with differing market capitalization, at various calendar periods, in conjunction with trading volume, or instances of changing investor sentiment, as evidenced by the put-call ratio. There is a statistically significant difference in the level and incidence of nonlinear behavior for the different-sized portfolios. Large-cap stocks exhibit the highest level and greatest incidence of nonlinear behavior, followed by mid-cap stocks, and then small-cap stocks. These differences are most pronounced at the beginning of decade and remain significant throughout the decade. For all size portfolios, nonlinear correlation increases throughout the decade, while linear correlation decreases. Statistical significance between the nonlinear or the linear test statistics and trading volume occur on a year-by-year basis only for small-cap stocks. There is sporadic seasonality significance for all portfolios over the decade, but only the small-cap portfolio consistently exhibits a notable "December effect". The average nonlinear statistic for small-cap stocks is larger in December than for other months of the year. The fourth quarter of the year for small-cap stocks also exhibits significantly higher levels of nonlinearity. An OLS regression of the put/call ratio to proxy for investor sentiment against the H and C statistic was run from October 1995 through December 2001. There are instances of sporadic correlations among the different portfolios, indicating this relationship is more dynamic than previously imagined. / Ph. D.

Gene set enrichment analysis aims to discover sets of genes, such as biological pathways or protein complexes, which may show moderate but coordinated differentiation across experimental conditions. The existing gene set enrichment approaches utilize single gene statistic as a measure of differentiation for individual genes. These approaches do not utilize any inter-gene correlations, but it has been known that genes in a pathway often interact with each other. Motivated by the need for taking gene dependence into account, we propose a novel gene set enrichment algorithm, where the gene-gene correlation is addressed via a gene-pair representation strategy. Relying on an appropriately defined gene pair statistic, the gene set statistic is formulated using a competitive null hypothesis. Extensive simulation studies show that our proposed approach can correctly control the type I error (false positive rate), and retain good statistical power for detecting true differential expression. The new method is also applied to analyze several gene expression datasets. / October 2016

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2018-01-19T10:48:06Z No. of bitstreams: 1 Felipe Nogueira Benetti.pdf: 858687 bytes, checksum: 4987e158a0496fbf988ca88a363a474b (MD5) / Made available in DSpace on 2018-01-19T10:48:06Z (GMT). No. of bitstreams: 1 Felipe Nogueira Benetti.pdf: 858687 bytes, checksum: 4987e158a0496fbf988ca88a363a474b (MD5) Previous issue date: 2017-12-12 / The objective of this study was to show the importance of statistical analysis and Big Data for the development of sport, especially soccer and the results obtained by the German team (specifically, the 2014 FIFA World Cup, in Brazil). The work covered the emergence of statistics and the types of analyses most used to obtain results with Big Data, passing through their definition and contributions to the daily lives of the population and companies that have access to the internet and smartphones. It was also was mentioned which sports modalities use the data volume processing with statistical analysis as a contribution to improve training and games. Finally, it was discussed the importance of the use of Big Data gave the German soccer team in conquering the World Cup in Brazil, what motives moved this investment and what results were obtained with this partnership. All the work was developed according to the standardization of the Brazilian Association of Technical Standards (ABNT, in portuguese) / O objetivo de estudo desta pesquisa foi mostrar a importância das análises estatísticas e do Big Data para o desenvolvimento do esporte, principalmente do futebol e os resultados obtidos pela seleção alemã (especificamente, a conquista da Copa do Mundo FIFA, em 2014). O trabalho abordou o surgimento da estatística e os tipos de análises mais utilizadas para a obtenção de resultados com Big Data, passando por sua definição e contribuições para o cotidiano da população e das empresas que possuem acesso à internet e a smartphones. Também foi mencionado quais modalidades esportivas utilizam o processamento de volume de dados com análises estatísticas como contribuição para melhorar treinos e partidas. Por fim, foi discutida a importância do uso do Big Data deu a seleção alemã de futebol na conquista da Copa do Mundo no Brasil, quais motivos moveram este investimento e quais resultados foram obtidos com essa parceria. Todo o trabalho foi desenvolvido de acordo com a normatização da Associação Brasileira de Normas Técnicas (ABNT)

In order to make statistical inference, that is drawing conclusions from a sample to describe a population, it is crucial to know the correct distribution of the data. This paper focused on censored data from the normal distribution. The purpose of this paper was to answer whether we can test if data comes from a censored normal distribution. This by using normality tests and tests designed for censored data and investigate if we got correct size of these tests. This has been carried out with simulations in the program R for left censored data. The results indicated that with increasing censoring normality tests failed to accept normality in a sample. On the other hand the censoring tests met the requirements with increasing censoring level, which was the most important conclusion in this paper.

Den här uppsatsen behandlar gammal DNA från 32 människor från den prehistoriska byn Ekwen belägen in nordöst Asien. Proverna har blivit masskopierade med hjälp av PCR och sekvenserad med FLX pyrosekvensering. Autentiska sekvenser har blivit bedömt genom användningen av PhyloNet och c-statistik och senare anpassad och jämförd med en referens sekvens (CRS). Tydliga C-T, T-C och A-G skador har upptäckts vid nukleotidpositioner vilket visar sig vara utmärkande för just den här populationen.

abstract: It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components are used to develop goodness-of-fit tests for models fit to the counts obtained from the cross-classification of multi-category dependent variables. Ordinal categories are assumed. Orthogonal components defined on marginals are obtained when analyzing multi-dimensional contingency tables through the use of the QR decomposition. A subset of these orthogonal components can be used to construct limited-information tests that allow one to identify the source of lack-of-fit and provide an increase in power compared to Pearson's test. These tests can address the adverse effects presented when data are sparse. The tests rely on the set of first- and second-order marginals jointly, the set of second-order marginals only, and the random forest method, a popular algorithm for modeling large complex data sets. The performance of these tests is compared to the likelihood ratio test as well as to tests based on orthogonal polynomial components. The derived goodness-of-fit tests are evaluated with studies for detecting two- and three-way associations that are not accounted for by a categorical variable factor model with a single latent variable. In addition the tests are used to investigate the case when the model misspecification involves parameter constraints for large and sparse contingency tables. The methodology proposed here is applied to data from the 38th round of the State Survey conducted by the Institute for Public Policy and Michigan State University Social Research (2005) . The results illustrate the use of the proposed techniques in the context of a sparse data set. / Dissertation/Thesis / Ph.D. Mathematics 2011

Feature extraction is an important step in the classification of high-dimensional data such as face images. Furthermore, linear feature extractors are more prevalent due to computational efficiency and preservation of the Gaussianity. This research proposes a simple and fast linear feature extractor approximating the sufficient statistic for Gaussian distributions. This method preserves the discriminatory information in both first and second moments of the data and yields the linear discriminant analysis as a special case. Additionally, an accurate upper bound on the error probability of a plug-in classifier can be used to approximate the number of features minimizing the error probability. Therefore, tighter error bounds are derived in this work based on the Bayes error or the classification error on the trained distributions. These bounds can also be used for performance guarantee and to determine the required number of training samples to guarantee approaching the Bayes classifier performance.

Um eletrodo de carbono vítreo foi modificado pela deposição de uma camada de nanotubos de carbono de paredes múltiplas, funcionalizados e decorados com nanopartículas de ouro. Este eletrodo foi caracterizado por microscopia ótica, mostrando uma superfície homogeneamente recoberta. Além disto, a sua morfologia foi investigada por microscopia eletrônica de transmissão, onde observou-se a distribuição e o tamanho médio aproximado de 20 nm das nanopartículas de ouro. Estas nanopartículas metálicas também foram caracterizadas por espectroscopia de absorção na região do UV-vis, mostrando um máximo de absorção em aproximadamente 525nm, o que confirma o seu tamanho médio de 20 nm. Os eletrodos modificados foram caracterizados eletroquimicamente pelo seu comportamento voltamétrico em uma solução de H2SO4 0,1 mol L-1, com uma velocidade de varredura de 0,100 V s-1. Nestes experimentos, ficou evidente os picos de formação e redução do óxido de ouro em potenciais acima de 0,8 V vs Ag/AgCl. Ainda foi observado o bom funcionamento dos eletrodos pela resposta voltamétrica do par redox [Ru(NH3)6]Cl2 / [Ru(NH3)6]Cl3 em meio de KCl. O desempenho deste eletrodo modificado para a oxidação dos pesticidas carbaril, etil-paration, malation e carbendazim foi investigado por voltametria de onda quadrada em tampão fosfato pH 7, porém apenas o inseticida e fungicida carbendazim mostrou eletroatividade. Desta forma, os estudos posteriores se focaram neste pesticida. O voltamograma cíclico do carbendazim mostrou um pico de oxidação e, na varredura reversa, um pico bem menor de redução. Isto sugeriu um mecanismo EC e um esquema da reação de oxidação foi proposto. Com o perfil voltamétrico estabelecido, a voltametria de onda quadrada foi utilizada para a determinação da curva analítica para o pesticida. Com todos os parâmetros da voltametria de onda quadrada otimizados, uma dependência linear da corrente de pico de oxidação com a concentração de carbendazim foi obtida, com a equação: Ip = 0,1 + 4,30 [carbendazim], com r2 = 0,9911 (n = 5). Esta curva analítica mostrou que a metodologia apresenta um limite de detecção de 17 x 10-8 mol L-1. Esta metodologia foi empregada na determinação de carbendazim em amostras de suco de laranja contaminadas artificialmente. A utilização de um teste t, de Student, mostrou que os valores recuperados pela voltametria não apresentaram qualquer diferença significante em relação àqueles adicionados às amostras. Assim, esta metodologia foi validada para a utilização na análise de suco de laranja contaminado com carbendazim / A glassy carbon electrode was modified by depositing a layer of multi-walled carbon nanotubes, functionalized and decorated with gold nanoparticles. This electrode was characterized by optical microscopy, showing a evenly coated surface. In addition, the morphology was investigated by transmission electron microscopy, where the distribution and the average size of 20 nm of the gold nanoparticles were observed. These metal nanoparticles were also characterized by absorption spectroscopy in the UV-vis region showing an absorption maximum at approximately 525 nm, which confirms their average size of 20 nm. The modified electrodes were electrochemically characterized by its voltammetric behavior in a 0.1 mol L-1H2SO4 solution, with a scanning rate of 0.100 V s-1. In these experiments, it became clear the formation and reduction of gold oxide peaks at potentials above 0.8 V vs. Ag/AgCl. It was also observed the proper functioning of the electrodes for the voltammetric response of the redox couple [Ru(NH3)6]Cl2/[Ru(NH3)6]Cl3 in a KCl electrolyte. The performance of the modified electrode for the oxidation of the pesticides: carbaryl, ethyl-parathion, malathion and carbendazim was investigated by square wave voltammetry in phosphate buffer, pH 7, but only the insecticide and fungicide carbendazim showed electroactivity. Thus, future studies focused on this pesticide. The cyclic voltammogram of carbendazim in phosphate buffer showed an oxidation peak and in the reverse scan, a much smaller reduction one. It suggested an EC mechanism and an oxidation reaction scheme was proposed. With the voltammetric profile established, square wave voltammetry was used to determine the calibration curve for the pesticide. With all square wave voltammetric parameters optimized, a linear dependence of the oxidation peak current with the concentration of carbendazim was obtained, with equation: Ip = 0.1 ± 4.30 [carbendazim] with r2 = 0, 9911 (n = 5). This calibration curve showed that the method has a detection limit of 17 x 10-8mol L-1. This methodology was used in the determination of carbendazim in orange juice samples artificially contaminated. A t-test of Student showed that the amounts recovered by voltammetry showed no significant difference in relation to those added to the samples. Thus, this methodology has been validated for use in analysis of orange juice contaminated with carbendazim

O FBST (Full Bayesian Significance Test) é um instrumento desenvolvido por Pereira e Stern (1999) com o objetivo de apresentar uma alternativa bayesiana aos testes de hipóteses precisas. Desde sua introdução, o FBST se mostrou uma ferramenta muito útil para a solução de problemas para os quais não havia soluções freqüentistas. Esse teste, contudo, depende de que a amostra seja coletada uma única vez, após o que a distribuição a posteriori dos parâmetros é obtida e a medida de evidência, calculada. Ensejadas por esse aspecto, são apresentadas abordagens analíticas e computacionais para a extensão do FBST ao contexto de decisão seqüencial (DeGroot, 2004). É apresentado e analisado um algoritmo para a execução do FBST Seqüencial, bem como o código-fonte de um software baseado nesse algoritmo. / FBST (Full Bayesian Significance Test) is a tool developed by Pereira and Stern (1999), to show a bayesian alternative to the tests of precise hypotheses. Since its introduction, FBST has shown to be a very useful tool to solve problems to which there were no frequentist solutions. This test, however, needs that the sample be collected just one time and, after this, the parameters posterior distribution is obtained and the evidence measure, computed. Suggested by this feature, there are presented analytic and computational approaches to the extension of the FBST to the sequential decision context (DeGroot, 2004). It is presented and analyzed an algorithm to execute the Sequential FBST, as well as the source code of a software based on this algorithm.