Made available in DSpace on 2016-06-02T20:04:51Z (GMT). No. of bitstreams: 1 4292.pdf: 1258858 bytes, checksum: c7d8d771d500d5ab8d54fbaae144001b (MD5) Previous issue date: 2012-02-07 / Financiadora de Estudos e Projetos / In this work we present a Bayesian analysis for bivariate survival data in the presence of a covariate and censored observations. We propose a bivariate distribution for the bivariate survival times based on the Farlie-Gumbel-Morgenstern (FGM) copula to model data with weak dependence. Some survival models with and without cure rate have been assumed for the marginal distributions. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) was considered. Further, some discussions on model selection criteria are given and comparisons with other copula models were performed. To detect influential observations in the data we consider a Bayesian case deletion influence diagnostics based on the -divergence. The OpenBUGS and R systems were used to simulate samples of the posterior distribution. Numerical illustrations are presented considering artificial and real data sets. / Neste trabalho apresentamos uma análise bayesiana para dados de sobrevivência bivariados na presença de covariáveis e observações censuradas. Propomos uma distribuição bivariada para os tempos de sobrevivência baseada na cópula de Farlie- Gumbel-Morgenstern (FGM) para modelar dados com fraca dependência. Alguns modelos de sobrevivência com e sem fração de cura foram assumidos para as distribuições marginais. Para fins inferenciais foi considerada uma abordagem bayesiana usando métodos Monte Carlo em Cadeias de Markov (MCMC). Além disso, algumas discussões sobre os critérios de seleção de modelos são apresentadas e comparações com outras cópulas foram realizadas. A fim de detectar observações influentes nos dados analisados foi utilizado o método bayesiano de análise de influência caso a caso baseado na divergência. Os sistemas OpenBUGS e R foram utilizados para simular amostras da distribuição a posteriori de interesse. Ilustrações numéricas são apresentadas considerando conjunto de dados artificiais e reais.

Cognitive radio is a technology that aims to maximize the current usage of the licensed frequency spectrum. Cognitive radio aims to provide services for license-exempt users by making use of dynamic spectrum access (DSA) and opportunistic spectrum sharing strategies (OSS). Cognitive radios are defined as intelligent wireless devices capable of adapting their communication parameters in order to operate within underutilized bands while avoiding causing interference to licensed users. An underused band of frequencies in a specific location or time is known as a spectrum hole. Therefore, in order to locate spectrum holes, reliable spectrum sensing algorithms are crucial to facilitate the evolution of cognitive radio networks. Since a large and growing body of literature has mainly focused into the conventional time domain (TD) energy detector, throughout this thesis the problem of spectrum sensing is investigated within the context of a frequency domain (FD) approach. The purpose of this study is to investigate detection based on methods of nonparametric power spectrum estimation. The considered methods are the periodogram, Bartlett's method, Welch overlapped segments averaging (WOSA) and the Multitaper estimator (MTE). Another major motivation is that the MTE is strongly recommended for the application of cognitive radios. This study aims to derive the detector performance measures for each case. Another aim is to investigate and highlight the main differences between the TD and the FD approaches. The performance is addressed for independent and identically distributed (i.i.d.) Rayleigh channels and the general Rician and Nakagami fading channels. For each of the investigated detectors, the analytical models are obtained by studying the characteristics of the Hermitian quadratic form representation of the decision statistic and the matrix of the Hermitian form is identified. The results of the study have revealed the high accuracy of the derived mathematical models. Moreover, it is found that the TD detector differs from the FD detector in a number of aspects. One principal and generalized conclusion is that all the investigated FD methods provide a reduced probability of false alarm when compared with the TD detector. Also, for the case of periodogram, the probability of sensing errors is independent of the length of observations, whereas in time domain the probability of false alarm is increased when the sample size increases. The probability of false alarm is further reduced when diversity reception is employed. Furthermore, compared to the periodogram, both Bartlett method and Welch method provide better performance in terms of lower probability of false alarm but an increased probability of detection for a given probability of false alarm. Also, the performance of both Bartlett's method and WOSA is sensitive to the number of segments, whereas WOSA is also sensitive to the overlapping factor. Finally, the performance of the MTE is dependent on the number of employed discrete prolate spheroidal (Slepian) sequences, and the MTE outperforms the periodogram, Bartlett's method and WOSA, as it provides the minimal probability of false alarm.