Combination of p-values from multiple independent tests has been widely studied since 1930's. To find the optimal combination methods, various combiners such as Fisher's method, inverse normal transformation, maximal p-value, minimal p-value, etc. have been compared by different criteria. In this work, we focus on the criterion of Bahadur efficiency, and compare various methods under the TFisher. As a recently developed general family of combiners, TFisher cover Fisher's method, the rank truncated product method (RTP), the truncation product method (TPM, or the hard-thresholding method), soft-thresholding method, minimal p-value method, etc. Through the Bahadur asymptotics, we better understand the relative performance of these methods. In particular, through calculating the Bahadur exact slopes for the problem of detecting sparse signals, we reveal the relative advantages of truncation versus non-truncation, hard-thresholding versus soft-thresholding. As a result, the soft thresholding method is shown superior when signal strength is relatively weak and the ratio between the sample size of each p-value and the number of combining p-values is small.

The method of combination p-values from multiple tests is the foundation for some studies like meta-analysis and detection of signal. There are tremendous methods have been developed and applied like minimum p-values, Cauchy Combination, goodness-of-fit combination and Fisher’s combination. In this paper, I tested their ability to detect signals which is related to real case in biology to find out significant single-nucleotide polymorphisms (SNPs). I simulated p-values for SNPs logistics regression model and test 7 combination methods’ power performance in different setting conditions. I compared sparse or dense signals, dependent or independent and combine them in gene-level or pathway-level. One method based on Fisher’s combination called Omni-TFisher is ideal for most of the situations. Recent years, genome-wide association studies (GWASs) focused on BMD-related SNPs at gene significance level. In this paper I used Omni-TFisher to analyses real data on haplotype blocks. As a result, haplotype blocks can find more SNPs in non-coding and intergeneric regions than gene-based and save computational complexity. It finds out not only known genes, but also other genes need further verification.

Made available in DSpace on 2014-12-17T15:47:44Z (GMT). No. of bitstreams: 1 RaulBP.pdf: 1811907 bytes, checksum: 007d54350318472b95b8e06144b749a5 (MD5) Previous issue date: 2007-07-27 / Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity / Apesar de algumas t?cnicas individuais de Aprendizado de M?quina (AM) supervisionado, tamb?mconhecidos como classificadores, ou algoritmos de classifica??o, fornecerem solu??es que, na maioria das vezes, s?o consideradas eficientes, h? resultados experimentais obtidos com a utiliza??o de grandes conjuntos de padr?es e/ou que apresentam uma quantidade expressiva de dados incompletos ou caracter?sticas irrelevantes, que mostram uma queda na efic?cia da precis?o dessas t?cnicas. Ou seja, tais t?cnicas n?o conseguem realizar um reconhecimento de padr?es de uma forma eficiente em problemas complexos. Com o intuito de obter um melhor desempenho e efic?cia dessas t?cnicas de AM, pensouse na id?ia de fazer com que v?rios tipos de algoritmos de AM consigam trabalhar conjuntamente, dando assim origem ao termo Sistema Multi-Classificador (SMC). Os SMC s apresentam, como componentes, diferentes algoritmos de AM, chamados de classificadores base, e realizam uma combina??o dos resultados obtidos por estes algoritmos para atingir o resultado final. Para que o SMC tenha um desempenho melhor que os classificadores base, os resultados obtidos por cada classificador base devem apresentar uma determinada diversidade, ou seja, uma diferen?a entre os resultados obtidos por cada classificador que comp?em o sistema. Pode-se dizer que n?o faz sentido ter SMC s cujos classificadores base possuam respostas id?nticas aos padr?es apresentados. Apesar dos SMC s apresentarem melhores resultados que os sistemas executados individualmente, h? sempre a busca para melhorar os resultados obtidos por esse tipo de sistema. Visando essa melhora e uma maior consist?ncia nos resultados, assim como uma maior diversidade dos classificadores de um SMC, v?m sendo recentemente pesquisadas metodologias que apresentam como caracter?sticas o uso de pesos, ou valores de con- fian?a. Esses pesos podem descrever a import?ncia que um determinado classificador forneceu ao associar cada padr?o a uma determinada classe. Esses pesos ainda s?o utilizados, em conjunto com as sa?das dos classificadores, durante o processo de reconhecimento (uso) dos SMC s. Existem diferentes maneiras de se calcular esses pesos e podem ser divididas em duas categorias: os pesos est?ticos e os pesos din?micos. A primeira categoria de pesos se caracteriza por n?o haver a modifica??o de seus valores no decorrer do processo de classifica??o, ao contr?rio do que ocorre com a segunda categoria, onde os valores sofrem modifica??es no decorrer do processo de classifica??o. Neste trabalho ser? feito uma an?lise para verificar se o uso dos pesos, tanto est?ticos quanto din?micos, conseguem aumentar o desempenho dos SMC s em compara??o com estes sistemas executados individualmente. Al?m disso, ser? feita uma an?lise na diversidade obtida pelos SMC s, para dessa forma verificar se h? alguma rela??o entre o uso dos pesos nos SMC s com diferentes n?veis de diversidade

This thesis is a study into the response and seismic safety of three-dimensional multi-storey concrete structures under concurrent orthogonal seismic excitations. It employs the nonlinear time-history method as its analysis tools. Time-history analyses rely heavily on their utilised earthquake records. Accordingly, this study examines the different approaches of selecting earthquake suites and develops a methodology of selecting representative earthquake scenarios. This methodology is credibly implemented in selecting a far- and a near field suites representative of the New Zealand seismic hazard. The study investigates the response of 6-, 9- and 12-storey concrete structures of different n-X-bays × m-Y-bays. Bidirectional responses of these considered structures are examined and consequently the current combination rules are scrutinised. Consequently this study strongly recommends the use of the 40-percent combination rule in lieu of the widely used 30-percent rule; and the use of time-history analysis in lieu of quasi/equivalent static and response modal analysis methods to avoid their strong dependence on combination rules. An intensive study is conducted employing the incremental dynamic analysis (IDA) technique to investigate structural demands of interstorey drifts, lateral storey drifts and storey accelerations. The study utilises the developed far-field suite and identifies the 50th and 90th percentile demands. Hence it provides easy-to-use expressions to facilitate rapid calculation of the structural demands and the effects of biaxial interactions. An implementation into the Demand and Capacity Factor Design (DCFD) format is presented that infers confidence in the performance levels of the considered structures. The study also draws attention to the importance of considering storey accelerations as their storey values reach as high as 10 × PGA. A sensitivity study is conducted by repeating the IDA investigation while using the developed near-field suite. Subsequently a comparison between the near- and the far-field results is conducted. The results were markedly similar albeit of less magnitudes until the (seismic hazard) intensity measure IM = Sa(T₁) = 0.4g when the near-field results show sudden flat large increase in demands suggesting a brittle collapse. This is attributed to the higher content of the higher mode frequencies contained in near-field ground motions. Finally, the study examines the (vectorial) radial horizontal shear demands in columns and beam-column joints of the previous far- and near-field studies. The combined radial shear demands in corner, edge and internal columns and joints are evaluated that roughly show a square-root proportional relationship with IM that exhibit somewhat brittle failure at IM ≥ 0.35g. Shears demands in the (4-way) internal columns and the (2-way) corner joints show highest magnitude in their respective class. The results suggest transverse joint shear reinforcement of 1.5, 1.0 and 0.5 of the longitudinal reinforcement of the neighbouring beam respectively for corner, edge and internal joints. An implementation of a proposed practical (and simpler) DCFD format shows satisfactory confidence in columns performance in shear up to IM = 0.35g, conversely to joints unsatisfactory performance in shear at the onset of inelastic behaviour (IM > 0.05g).