This thesis concerns the development, estimation and investigation of a general anisotropic spatial correlation function, within model-based geostatistics, expressed as a Gaussian linear mixed model, and estimated using residual maximum likelihood (REML). The Matern correlation function is attractive because of its parameter which controls smoothness of the spatial process, and which can be estimated from the data. This function is combined with geometric anisotropy, with an extension permitting different distance metrics, forming a flexible spatial covariance model which incorporates as special cases many infinite- range spatial covariance functions in common use. Derivatives of the residual log-likelihood with respect to the four correlation-model parameters are derived, and the REML algorithm coded in Splus for testing and refinement as a precursor to its implementation into the software ASReml, with additional generality of linear mixed models. Suggestions are given regarding initial values for the estimation. A residual likelihood ratio test for anisotropy is also developed and investigated. Application to three soil-based examples reveals that anisotropy does occur in practice, and that this technique is able to fit covariance models previously unavailable or inaccessible. Simulations of isotropic and anisotropic data with and without a nugget effect reveal the following principal points. Inclusion of some closely-spaced locations greatly improves estimation, particularly of the Matern smoothness parameter, and of the nugget variance when present. The presence of geometric anisotropy does not adversely affect parameter estimation. Presence of a nugget effect introduces greater uncertainty into the parameter estimates, most dramatically for the smoothness parameter, and also increases the chance of non-convergence and decreases the power of the test for anisotropy. Estimation is more difficult with very “unsmooth" processes (Matern smoothness parameter 0.1 or 0.25) | non- convergence is more likely and estimates are less precise and/or more biased. However it is still often possible to fit the full model including both anisotropy and nugget effect using REML with as few as 100 observations. Additional simulations involving model misspecification reveal that ignoring anisotropy when it is present can substantially increase the mean squared error of prediction, but overfitting by attempting to model anisotropy when it is absent is less damaging. Further, plug-in estimates of prediction error variance are reasonable estimates of the actual mean squared error of prediction, regardless of the model fitted, weakening the argument requiring Bayesian approaches to properly allow for uncertainty in the parameter estimates when estimating prediction error variance. The most valuable outcome of this research is the implementation of an anisotropic Matern correlation function in ASReml, including the full generality of Gaussian linear mixed models which permits additional fixed and random effects, making publicly available the facility to fit, via REML estimation, a much wider range of variance models than has previously been readily accessible. This greatly increases the probability and ease with which a well-fitting covariance model can be found for a spatial data set, thus contributing to improved geostatistical spatial analysis. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1297562 / Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2007

Epidemiological data with disease characteristic information can be modelled in several ways. One way is taking each disease characteristic as a response and constructing binary or polytomous logistic regression model. Second way is using a new response which consists of disease subtypes created by cross-classification of disease characteristic levels, and then constructing polytomous logistic regression model. The former may be disadvantageous since any possible covariation between disease characteristics is neglected, whereas the latter can capture that covariation behaviour. However, cross-classifying the characteristic levels increases the number of categories of response, so that dimensionality problem in parameter space may occur in classical polytomous logistic regression model. A two staged polytomous logistic regression model overcomes that dimensionality problem. In this thesis, study is progressen in two main directions: simulation study and data analysis parts. In simulation study, models that capture the covariation behaviour are compared in terms of the response model parameter estimators. That is, performances of the maximum likelihood estimation (MLE) approach to classical polytomous logistic regression, Bayesian estimation approach to classical polytomous logistic regression and pseudo-conditional likelihood (PCL) estimation approach to two stage polytomous logistic regression are compared in terms of bias and variation of estimators. Results of the simulation study revealed that for small sized sample and small number of disease subtypes, PCL outperforms in terms of bias and variance. For medium scaled size of total disease subtypes situation when sample size is small, PCL performs better than MLE, however when the sample size gets larger MLE has better performance in terms of standard errors of estimates. In addition, sampling variance of PCL estimators of two stage model converges to asymptotic variance faster than the ML estimators of classical polytomous logistic regression model. In data analysis, etiologic heterogeneity in breast cancer subtypes of Turkish female cancer patients is investigated, and the superiority of the two stage polytomous logistic regression model over the classical polytomous logistic model with disease subtypes is represented in terms of the interpretation of parameters and convenience in hypothesis testing.

The topic of this thesis is the problem of trademark infringement examined in the context of the development of legal jurisdiction. This also includes aspects of unfair competition, and in most of the cases a couple of factors are involved.I use Czechoslovakian, Czech, as well as some European court verdicts in relation to different legal regulations.

Os delineamentos sistemáticos são usados nas mais diversas áreas, como: florestal, horticultura, solos, etc. Na área florestal, os delineamentos sistemáticos são freqüentemente usados para estudos preliminares e têm o objetivo de testar o maior número de espaçamentos possíveis. No entanto, existem algumas limitações para a sua utilização. A primeira é o arranjo sistemático (não casualizado) das plantas, que não permite o uso das análises convencionais. A segunda é a alta sensibilidade para valores perdidos. Quando uma planta é perdida, o espaçamento das plantas vizinhas é alterado, assim esses valores não podem ser incluídos no conjunto de dados e informações consideráveis são excluídas das análises. O objetivo deste trabalho foi aplicar a metodologia geoestatística para identificação de dependência espacial em um experimento em delineamento sistemático tipo "leque", levando-se em consideração: a eliminação dos dados das plantas vizinhas aos valores perdidos e as informações de ocorrência de parcelas perdidas e o tempo que ocorreram. Os dados de volume sólido por planta utilizados neste trabalho são provenientes de um experimento de espaçamento de Eucalyptus dunnii em delineamento sistemático tipo "leque". Neste trabalho foram utilizados os dados referentes ao sexto ano, idade comercial de corte da espécie, com os seguintes procedimentos: eliminação dos dados das plantas vizinhas às plantas mortas (Modelo I); as informações de mortes das plantas foram consideradas como uma covariável no modelo (Modelo II) e além da covariável morte das plantas, também foi levado em consideração o tempo da ocorrência da morte (Modelo III). Os parâmetros do semivariograma foram estimados pelo método de máxima verossimilhança e para seleção de modelos, utilizou-se o Critério de Akaike (AIC). Os resultados obtidos permitem concluir que se identificou uma fraca dependência espacial, o que não justificaria considerá-la com a aplicação de um modelo geoestatístico. A função de correlação que apresentou melhor desempenho foi a Matérn com k = 2 para os três modelos considerados. Comparando-se esses modelos e seguindo o critério de Akaike, o modelo mais adequado foi o II, pois apresentou menor valor de AIC. / Systematic designs are utilized in many areas, such as: forestry, horticulture, soils, etc. In forestry, the systematic designs are frequently used for preliminary studies and they aim at evaluating the largest number of possible spacings. However, there are some limitations on their use. The first limitation is the systematic design (non-randomized) of plants, which does not allow the use of conventional analyses. The second is the high sensitivity to lost values. When a plant is lost, the neighboring plant spacings are altered, so these values cannot be added to the data collection, and a great sum of information is excluded from the analyses. This study aimed at applying geostatistical methods to identify the spatial dependence in the data analysis of a fan systematic design experiment, taking into account: the exclusion of neighboring plant data to the lost values and the information regarding the occurrence of lost parcels as well as the time of their occurrence. The plant solid volume data utilized in this study were taken from a fan systematic design Eucalyptus dunnii spacing study. The data utilized were referent to the sixth year, commercial age for cutting of the specie, with the following procedures exclusion of the data from a neighboring plant next to a dead tree (Model I); the information of tree mortality as covariable in the model (Model II); and the time of occurrence of tree mortality, besides the tree mortality covariable (Model III). The semivariogram parameters were estimated by the maximum likelihood method, and the model selection was done by the utilization of the Akaike's Information Criterion (AIC). It was possible to conclude from the result analyses that there is a weak spatial dependence, which does not justify neither taking it into account nor the utilization of a geostatistical model. The correlation function that showed the best performance was the Matérn, with kappa=2 for the three models considered. By the comparison of these three models and the utilization of the Akaike's Information Criterion, the most suitable model was Model II, as it showed lower AIC value.

Os delineamentos sistemáticos são usados nas mais diversas áreas, como: florestal, horticultura, solos, etc. Na área florestal, os delineamentos sistemáticos são freqüentemente usados para estudos preliminares e têm o objetivo de testar o maior número de espaçamentos possíveis. No entanto, existem algumas limitações para a sua utilização. A primeira é o arranjo sistemático (não casualizado) das plantas, que não permite o uso das análises convencionais. A segunda é a alta sensibilidade para valores perdidos. Quando uma planta é perdida, o espaçamento das plantas vizinhas é alterado, assim esses valores não podem ser incluídos no conjunto de dados e informações consideráveis são excluídas das análises. O objetivo deste trabalho foi aplicar a metodologia geoestatística para identificação de dependência espacial em um experimento em delineamento sistemático tipo "leque", levando-se em consideração: a eliminação dos dados das plantas vizinhas aos valores perdidos e as informações de ocorrência de parcelas perdidas e o tempo que ocorreram. Os dados de volume sólido por planta utilizados neste trabalho são provenientes de um experimento de espaçamento de Eucalyptus dunnii em delineamento sistemático tipo "leque". Neste trabalho foram utilizados os dados referentes ao sexto ano, idade comercial de corte da espécie, com os seguintes procedimentos: eliminação dos dados das plantas vizinhas às plantas mortas (Modelo I); as informações de mortes das plantas foram consideradas como uma covariável no modelo (Modelo II) e além da covariável morte das plantas, também foi levado em consideração o tempo da ocorrência da morte (Modelo III). Os parâmetros do semivariograma foram estimados pelo método de máxima verossimilhança e para seleção de modelos, utilizou-se o Critério de Akaike (AIC). Os resultados obtidos permitem concluir que se identificou uma fraca dependência espacial, o que não justificaria considerá-la com a aplicação de um modelo geoestatístico. A função de correlação que apresentou melhor desempenho foi a Matérn com k = 2 para os três modelos considerados. Comparando-se esses modelos e seguindo o critério de Akaike, o modelo mais adequado foi o II, pois apresentou menor valor de AIC. / Systematic designs are utilized in many areas, such as: forestry, horticulture, soils, etc. In forestry, the systematic designs are frequently used for preliminary studies and they aim at evaluating the largest number of possible spacings. However, there are some limitations on their use. The first limitation is the systematic design (non-randomized) of plants, which does not allow the use of conventional analyses. The second is the high sensitivity to lost values. When a plant is lost, the neighboring plant spacings are altered, so these values cannot be added to the data collection, and a great sum of information is excluded from the analyses. This study aimed at applying geostatistical methods to identify the spatial dependence in the data analysis of a fan systematic design experiment, taking into account: the exclusion of neighboring plant data to the lost values and the information regarding the occurrence of lost parcels as well as the time of their occurrence. The plant solid volume data utilized in this study were taken from a fan systematic design Eucalyptus dunnii spacing study. The data utilized were referent to the sixth year, commercial age for cutting of the specie, with the following procedures exclusion of the data from a neighboring plant next to a dead tree (Model I); the information of tree mortality as covariable in the model (Model II); and the time of occurrence of tree mortality, besides the tree mortality covariable (Model III). The semivariogram parameters were estimated by the maximum likelihood method, and the model selection was done by the utilization of the Akaike's Information Criterion (AIC). It was possible to conclude from the result analyses that there is a weak spatial dependence, which does not justify neither taking it into account nor the utilization of a geostatistical model. The correlation function that showed the best performance was the Matérn, with kappa=2 for the three models considered. By the comparison of these three models and the utilization of the Akaike's Information Criterion, the most suitable model was Model II, as it showed lower AIC value.

La synchronisation temporelle est la première opération effectuée par le démodulateur. Elle permet d'assurer que les échantillons transmis aux processus de démodulation puissent réaliser un taux d'erreurs binaires le plus faible.Dans cette thèse, nous proposons l'étude d'algorithmes innovants de synchronisation temporelle à haute performance.D'abord, nous avons proposé des algorithmes exploitant l'information souple du décodeur en plus du signal reçu afin d'améliorer l'estimation aveugle d'un retard temporel supposé constant sur la durée d'observation.Ensuite, nous avons proposé un algorithme original basé sur la synchronisation par lissage à faible complexité.Cette étape a consisté à proposer une technique opérant dans un contexte hors ligne, permettant l'estimation d'un retard aléatoire variable dans le temps via les boucles d'aller-retour sur plusieurs itération. Les performances d'un tel estimateur dépassent celles des algorithmes traditionnels.Afin d'évaluer la pertinence de tous les estimateurs proposés, pour des retards déterministe et aléatoire, nous avons évalué et comparé leurs performances à des bornes de Cramèr-Rao que nous avons développées pour ce cadre. Enfin, nous avons évalué les algorithmes proposés sur des signaux WBAN. / Time synchronization is the first function performed by the demodulator. It ensures that the samples transmitted to the demodulation processes allow to achieve the lowest bit error rate.In this thesis we propose the study of innovative algorithms for high performance time synchronization.First, we propose algorithms exploiting the soft information from the decoder in addition to the received signal to improve the blind estimation of the time delay. Next, we develop an original algorithm based on low complexity smoothing synchronization techniques. This step consisted in proposing a technique operating in an off-line context, making it possible to estimate a random delay that varies over time on several iterations via Forward- Backward loops. The performance of such estimators exceeds that of traditional algorithms. In order to evaluate the relevance of all the proposed estimators, for deterministic and random delays, we evaluated and compared their performance to Cramer-Rao bounds that we developed within these frameworks. We, finally, evaluated the proposed algorithms on WBAN signals.