Made available in DSpace on 2017-07-10T19:24:25Z (GMT). No. of bitstreams: 1 JOELMIR ANDRE BORSSOI.pdf: 1897222 bytes, checksum: 4bfaafc0659eed32ceef8dc0fe90a8fe (MD5) Previous issue date: 2007-12-04 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Tracking and management concepts of the process of agricultural production are being used as a great option of strategy management in agriculture. Such concepts consider the spatial variability of the variables at study. The modeling of the spatial dependence structure of the geoestatistic approach is fundamental importance for the definition parameters that define this structure and are used in the interpolation of values in places not sampled, by kriging techniques. However, the estimation of parameters can be greatly affected by the presence atypical observations in the data sampled. The development of this work was aimed at using diagnostics techniques in spatial linear gaussians models, used in geoestatistics, to evaluate the sensitivity of the maximum likelihood estimators and restrict maximum likelihood to small perturbations in the data. Studies were performed with simulated data, with literature data and with experimental data, collected in a commercial agricultural area in the region West of Paraná. The study with simulated data showed that the techniques used in diagnostics were efficient in identifying the perturbation data. The restrict maximum likelihood estimator produced more robust estimates for the parameters spatial dependence. Those results obtained from the study of real data, it was concluded that the presence atypical values between the sampled data can exert strong influence on thematic maps, changing, therefore, the spatial dependence. The application the diagnostic techniques should be part of any geoestatistic analysis, ensuring that the information contained in thematic maps have better quality and can be used with greater security by the farmer. / Conceitos de monitoramento e gerenciamento do processo de produção agrícola vêm sendo utilizados como ótima opção de estratégia gerencial na agricultura. Tais conceitos consideram a variabilidade espacial das variáveis em estudo. A modelagem da estrutura de dependência espacial pela abordagem da geoestatística é de fundamental importância para a definição de parâmetros que definem esta estrutura e que são utilizados na interpolação de valores em locais não amostrados, pela técnica de krigagem. Entretanto, a estimação de parâmetros pode ser muito afetada pela presença de observações atípicas nos dados amostrados. O desenvolvimento deste trabalho teve por objetivo utilizar técnicas de diagnóstico em modelos espaciais lineares gaussianos, utilizados em geoestatística, para avaliar a sensibilidade dos estimadores máxima verossimilhança e máxima verossimilhança restrita a pequenas perturbações nos dados. Realizaram-se estudos com dados simulados, com dados da bibliografia e também com dados experimentais, coletados em uma área agrícola comercial da região Oeste do Paraná. O estudo com dados simulados mostrou que as técnicas de diagnóstico utilizadas foram eficientes na identificação da perturbação nos dados. O estimador de máxima verossimilhança restrita produziu estimativas mais robustas para os parâmetros de dependência espacial. Pelos resultados obtidos com o estudo de dados reais, concluiu-se que a presença de valores atípicos entre os dados amostrados pode exercer forte influência nos mapas temáticos, alterando, assim, a dependência espacial. A aplicação de técnicas de diagnóstico deve fazer parte de toda análise geoestatística, garantindo que as informações contidas nos mapas temáticos tenham maior qualidade e possam ser utilizadas com maior segurança pelo agricultor.

Made available in DSpace on 2017-05-12T14:47:42Z (GMT). No. of bitstreams: 1 JOELMIR ANDRE BORSSOI.pdf: 1897222 bytes, checksum: 4bfaafc0659eed32ceef8dc0fe90a8fe (MD5) Previous issue date: 2007-12-04 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Tracking and management concepts of the process of agricultural production are being used as a great option of strategy management in agriculture. Such concepts consider the spatial variability of the variables at study. The modeling of the spatial dependence structure of the geoestatistic approach is fundamental importance for the definition parameters that define this structure and are used in the interpolation of values in places not sampled, by kriging techniques. However, the estimation of parameters can be greatly affected by the presence atypical observations in the data sampled. The development of this work was aimed at using diagnostics techniques in spatial linear gaussians models, used in geoestatistics, to evaluate the sensitivity of the maximum likelihood estimators and restrict maximum likelihood to small perturbations in the data. Studies were performed with simulated data, with literature data and with experimental data, collected in a commercial agricultural area in the region West of Paraná. The study with simulated data showed that the techniques used in diagnostics were efficient in identifying the perturbation data. The restrict maximum likelihood estimator produced more robust estimates for the parameters spatial dependence. Those results obtained from the study of real data, it was concluded that the presence atypical values between the sampled data can exert strong influence on thematic maps, changing, therefore, the spatial dependence. The application the diagnostic techniques should be part of any geoestatistic analysis, ensuring that the information contained in thematic maps have better quality and can be used with greater security by the farmer. / Conceitos de monitoramento e gerenciamento do processo de produção agrícola vêm sendo utilizados como ótima opção de estratégia gerencial na agricultura. Tais conceitos consideram a variabilidade espacial das variáveis em estudo. A modelagem da estrutura de dependência espacial pela abordagem da geoestatística é de fundamental importância para a definição de parâmetros que definem esta estrutura e que são utilizados na interpolação de valores em locais não amostrados, pela técnica de krigagem. Entretanto, a estimação de parâmetros pode ser muito afetada pela presença de observações atípicas nos dados amostrados. O desenvolvimento deste trabalho teve por objetivo utilizar técnicas de diagnóstico em modelos espaciais lineares gaussianos, utilizados em geoestatística, para avaliar a sensibilidade dos estimadores máxima verossimilhança e máxima verossimilhança restrita a pequenas perturbações nos dados. Realizaram-se estudos com dados simulados, com dados da bibliografia e também com dados experimentais, coletados em uma área agrícola comercial da região Oeste do Paraná. O estudo com dados simulados mostrou que as técnicas de diagnóstico utilizadas foram eficientes na identificação da perturbação nos dados. O estimador de máxima verossimilhança restrita produziu estimativas mais robustas para os parâmetros de dependência espacial. Pelos resultados obtidos com o estudo de dados reais, concluiu-se que a presença de valores atípicos entre os dados amostrados pode exercer forte influência nos mapas temáticos, alterando, assim, a dependência espacial. A aplicação de técnicas de diagnóstico deve fazer parte de toda análise geoestatística, garantindo que as informações contidas nos mapas temáticos tenham maior qualidade e possam ser utilizadas com maior segurança pelo agricultor.

The Brachiopoda, a major group of the Lophotrochozoa, experienced a rapid early evolutionary diversification during the well-known Cambrian explosion and subsequently dominated the Palaeozoic benthos with its diversity and abundance. Even though the phylogeny of the Lophotrochozoa is still hotly debated, it is now known that the Brachiopoda are a monophyletic grouping. However, the early evolutionary rates for the Brachiopoda have never been studied in the framework of a study combining molecular data and fossil time calibration points. In order to investigate the expected higher evolutionary rates of the Phylum at its origin, we conducted phylogenetic studies combining different methodologies and datasets. This work has at its foundation Maximum Likelihood and Bayesian analyses of 18S and 28S rRNA datasets followed by analyses of phylogenomic sequences. All material was obtained from previously available sequences and from sequencing of genetic material from specimens from a concerted worldwide collection effort.       While the analyses of the phylogenomic dataset produced a robust phylogeny of the Brachiopoda with good support, both the results of the novel rRNA and phylogenomic dating analyses provided limited insights into the early rates of evolution of the Brachiopoda from a newly assembled dataset, demonstrating some limitations in calibration dating using the software package BEAST2. Future studies implementing fossil calibration, possibly incorporating morphological data, should be attempted to elucidate the early rates of evolution of Brachiopoda and the effect of the Push of the Past in this clade. / Det är ofta antaget att evolution (förändringar i arvsmassan hos en grupp organismer) sker i en konstant hastighet men i slutändan ändå osäkert om så är fallet. Stora grupper av organismer har ofta associerats med en högre evolutionär hastighet, speciellt nära deras uppkomst, vilket ökar sannolikheten för överlevnad.  Armfotingar (Brachiopoda) är marina ryggradslösa djur med skal som tidigare var allmänt spridd, idag är istället musslor (Bivalvia) betydligt mer spridda. Armfotingar har funnits och utvecklats under flera miljoner år med ursprung under tidigt kambrium. Genom år av forskning och många fossil har vi fått mer information om utseendet hos utdöda organismer vilket har bidragit till att antalet fossila arter som vi känner till har ökat tusenfalt. Under den senaste tiden har det också skett innovationer inom molekylära tekniker som gjort det möjligt att applicera dessa kunskaper även på utdöda arter. Dessa molekylära tekniker har nyligen hjälpt till att bestämma några av släktskapsförhållandena inom armfotingar som tidigare ansetts vara väldigt svåra att lösa.  Det finns fortfarande vissa släktskapsförhållanden inom armfotingar som inte är kända och man vet ännu inte hur fort de utvecklades. Genom att undersöka just evolutionens hastighet kan man börja förstå gruppens tidiga framgång under Kambrium och Ordovicium samt minskningen som följde. Syftet med den här studien var att beräkna evolutionshastigheten hos armfotingar med särskild fokus på den tidiga diversifieringen av gruppen. För att undersöka detta använde vi oss av molekylära data för att analysera släktskapsförhållandena inom armfotingar. Dessutom använde vi fossil för att datera stora händelser i armfotingarnas evolutionära historia. Med hjälp av statistiska analyser kunde vi beräkna evolutionshastighet och släktskapsförhållandena inom gruppen. Vi kom fram till att armfotingar härstammar från en gemensam förfader. Dateringen kring när detta skedde blev inte fastställd då det beräknades ske miljoner år före det äldsta djurfossilet. Det kommer behövas mer forskning för att ta reda på om armfotingar hade en högre evolutionär hastighet i tidigt skede.

The focus in this thesis is on the calculations of an empirical null distributionfor likelihood ratio tests testing either separable or double separable covariancematrix structures versus an unstructured covariance matrix. These calculationshave been performed for various dimensions and sample sizes, and are comparedwith the asymptotic χ2-distribution that is commonly used as an approximative distribution. Tests of separable structures are of particular interest in cases when data iscollected such that more than one relation between the components of the observationis suspected. For instance, if there are both a spatial and a temporalaspect, a hypothesis of two covariance matrices, one for each aspect, is reasonable.

In 1988, Owen introduced empirical likelihood as a nonparametric method for constructing confidence intervals and regions. Since then, empirical likelihood has been studied extensively in the literature due to its generality and effectiveness. It is well known that empirical likelihood has several attractive advantages comparing to its competitors such as bootstrap: determining the shape of confidence regions automatically using only the data; straightforwardly incorporating side information expressed through constraints; being Bartlett correctable. The main part of this thesis extends the empirical likelihood method to several interesting and important statistical inference situations. This thesis has four components. The first component (Chapter II) proposes a smoothed jackknife empirical likelihood method to construct confidence intervals for the receiver operating characteristic (ROC) curve in order to overcome the computational difficulty when we have nonlinear constrains in the maximization problem. The second component (Chapter III and IV) proposes smoothed empirical likelihood methods to obtain interval estimation for the conditional Value-at-Risk with the volatility model being an ARCH/GARCH model and a nonparametric regression respectively, which have applications in financial risk management. The third component(Chapter V) derives the empirical likelihood for the intermediate quantiles, which plays an important role in the statistics of extremes. Finally, the fourth component (Chapter VI and VII) presents two additional results: in Chapter VI, we present an interesting result by showing that, when the third moment is infinity, we may prefer the Student's t-statistic to the sample mean standardized by the true standard deviation; in Chapter VII, we present a method for testing a subset of parameters for a given parametric model of stationary processes.

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

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Given the ever-growing demand for radio spectrum, cognitive radio has recently emerged as an attractive wireless communication technology. This dissertation is concerned with developing spectrum sensing algorithms in cognitive radio networks where a single or multiple cognitive radios (CRs) assist in detecting licensed primary bands employed by single or multiple primary users. First, given that orthogonal frequency-division multiplexing (OFDM) is an important wideband transmission technique, detection of OFDM signals in low-signal-to-noise-ratio scenario is studied. It is shown that the cyclic prefix correlation coefficient (CPCC)-based spectrum sensing algorithm, which was previously introduced as a simple and computationally efficient spectrum-sensing method for OFDM signals, is a special case of the constrained generalized likelihood ratio test (GLRT) in the absence of multipath. The performance of the CPCC-based algorithm degrades in a multipath scenario. However when OFDM is implemented, by employing the inherent structure of OFDM signals and exploiting multipath correlation in the GLRT algorithm a simple and low-complexity algorithm called the multipath-based constrained-GLRT (MP-based C-GLRT) algorithm is obtained. Further performance improvement is achieved by combining both the CPCC- and MP-based C-GLRT algorithms. A simple GLRT-based detection algorithm is also developed for unsynchronized OFDM signals. In the next part of the dissertation, a cognitive radio network model with multiple CRs is considered in order to investigate the benefit of collaboration and diversity in improving the overall sensing performance. Specially, the problem of decision fusion for cooperative spectrum sensing is studied when fading channels are present between the CRs and the fusion center (FC). Noncoherent transmission schemes with on-off keying (OOK) and binary frequency-shift keying (BFSK) are employed to transmit the binary decisions to the FC. The aim is to maximize the achievable secondary throughput of the CR network. Finally, in order to reduce the required transmission bandwidth in the reporting phase of the CRs in a cooperative sensing scheme, the last part of the dissertation examines nonorthogonal transmission of local decisions by means of on-off keying. Proposed and analyzed is a novel decoding-based fusion rule for combining the hard decisions in a linear manner.

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com