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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Analysis of Poisson count time series with unknown periodicity

Jervis, Sarah January 2011 (has links)
No description available.
2

Monitoramento de séries de contagem por meio de gráficos de controle / Monitoring time series of counts using control charts..

Esparza Albarracin, Orlando Yesid 10 March 2014 (has links)
Na área da saúde, várias abordagens nos últimos anos têm sido propostas baseadas nos gráficos de controle CUSUM para a detecção de epidemias infecciosas em que a caraterística a ser monitorada é uma série temporal de dados de contagem, como o número de internações. Neste trabalho foram implementados os modelos lineares generalizados (MLG) no monitoramento, por meio dos gráficos CUSUM e Shewhart, da série do número diário de internações por causas respiratórias para pessoas com 65 anos ou mais residentes no município de São Paulo. Por meio de simulações, avaliaram-se a eficiência de cinco estatísticas diferentes para detectar mudanças na média em séries de contagem. Uma das abordagens consistiu na implementação de três transformações normalizadoras simples que dependem unicamente dos parâmetros das distribuições Poisson e binomial negativa: a transformação Rossi para dados com distribuição Poisson, a transformação Jorgensen para dados com distribuição binomial negativa e os sesíduos de Anscombe para modelos lineares generalizados. As duas últimas estatísticas já foram propostas como gráficos CUSUM: o Método Rogerson e Yamada (2004) é apresentado para dados com distribuição Poisson e neste trabalho foi proposto um novo parâmetro kt para dados binomial negativa; já o método proposto por Hohle (2007) é baseado na função de verossimilhança da distribuição binomial negativa. Utilizando limites de controle para obter um valor ARL0 = 500 sob normalidade, monitorou-se via simulação a série de interesse, implementando as transformações normalizadoras. Entretanto, utilizando-se esses limiares observa-se um maior número de alarmes falsos para as três estatísticas. Modificando o parâmetro k do gráfico CUSUM permitindo que variasse ao longo do tempo a série foi monitorada e foram obtidos valores ARL0 próximos a 500. Os gráficos CUSUM baseados no método Rogerson e Yamada e na estatística da razão de verossimilhanças para dados com distribuição binomial negativa mostraram, via simulação, bons resultados para detectar mudanças na média. As suposições de normalidade e independência das estatísticas normalizadoras, em geral omitidas em trabalhos publicados na literatura, foram avaliadas e comprova-se que as transformações não normalizam os dados, porém são independentes e estacionárias. Analisando os dados reais, as estatísticas apresentaram autocorrelação significativa no lag 7. Devido à persistência desta autocorrelação, foi proposta uma abordagem baseada no ajuste do modelo GARMA. / In public health several approaches have been proposed for the detection of outbreaks of infectious diseases where the characteristic being monitored is a time series of count data as the number of hospitalizations, where the population and the expected rate of admissions change over time. In this work we fitted generalized linear models (GLM) and implemented Shewhart and CUSUM control charts for monitoring the daily number of hospital admissions due to respiratory diseases for people aged 65 and older in the city of São Paulo. Through simulations, we evaluated the efficiency of implementing five different statistical for detecting changes in time series of count. One approach consisted of applying three transformations that only depend on the parameters of the negative binomial and Poisson distributions: The transformations of Rossi for data with Poisson distribution, the transformation proposed by Jorgensen for data with negative binomial distribution and residuals proposed by Anscombe for generalized linear models. The other statistics have been proposed as CUSUM charts: the method of Rogerson e Yamada (2004) was presented for data with Poisson distribution, in this work we proposed a new parameter kt for negative binomial distribution, the proposed method for Hohle (2007) uses the likelihood ratio statistic. Implementing limit control assuming normality for a value of ARL0 = 500 be monitored via simulation the serie of interest implementing the normalizing statistics. However, using these limits was observed a greater number of alarms for the three transformations. Modifying the parameter k of the CUSUM chart to this change over time the series was monitored and were obtained values of ARL0 close to 500. The CUSUM control charts for the methods of Rogerson and Yamada and Holhe for data with negative binomial distribution showed, by simulation, good results for detecting changes in the mean. For negative binomial distribution generalizing the method of Rogerson e Yamada (2004) and implemented the CUSUM charts using the likelihood ratio statistic. Both methods provided good results via simulation to detect small changes in average. The evaluation of assumptions of normality for the statistics proposed by Rossi, Jorgensen and Anscombe generally is omitted in published studies. In this work, we evaluated this assumptions indicating that the statistics are not normal using the real dataset but are independent and stationary. By analyzing real data, due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent. Due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent.
3

Monitoramento de séries de contagem por meio de gráficos de controle / Monitoring time series of counts using control charts..

Orlando Yesid Esparza Albarracin 10 March 2014 (has links)
Na área da saúde, várias abordagens nos últimos anos têm sido propostas baseadas nos gráficos de controle CUSUM para a detecção de epidemias infecciosas em que a caraterística a ser monitorada é uma série temporal de dados de contagem, como o número de internações. Neste trabalho foram implementados os modelos lineares generalizados (MLG) no monitoramento, por meio dos gráficos CUSUM e Shewhart, da série do número diário de internações por causas respiratórias para pessoas com 65 anos ou mais residentes no município de São Paulo. Por meio de simulações, avaliaram-se a eficiência de cinco estatísticas diferentes para detectar mudanças na média em séries de contagem. Uma das abordagens consistiu na implementação de três transformações normalizadoras simples que dependem unicamente dos parâmetros das distribuições Poisson e binomial negativa: a transformação Rossi para dados com distribuição Poisson, a transformação Jorgensen para dados com distribuição binomial negativa e os sesíduos de Anscombe para modelos lineares generalizados. As duas últimas estatísticas já foram propostas como gráficos CUSUM: o Método Rogerson e Yamada (2004) é apresentado para dados com distribuição Poisson e neste trabalho foi proposto um novo parâmetro kt para dados binomial negativa; já o método proposto por Hohle (2007) é baseado na função de verossimilhança da distribuição binomial negativa. Utilizando limites de controle para obter um valor ARL0 = 500 sob normalidade, monitorou-se via simulação a série de interesse, implementando as transformações normalizadoras. Entretanto, utilizando-se esses limiares observa-se um maior número de alarmes falsos para as três estatísticas. Modificando o parâmetro k do gráfico CUSUM permitindo que variasse ao longo do tempo a série foi monitorada e foram obtidos valores ARL0 próximos a 500. Os gráficos CUSUM baseados no método Rogerson e Yamada e na estatística da razão de verossimilhanças para dados com distribuição binomial negativa mostraram, via simulação, bons resultados para detectar mudanças na média. As suposições de normalidade e independência das estatísticas normalizadoras, em geral omitidas em trabalhos publicados na literatura, foram avaliadas e comprova-se que as transformações não normalizam os dados, porém são independentes e estacionárias. Analisando os dados reais, as estatísticas apresentaram autocorrelação significativa no lag 7. Devido à persistência desta autocorrelação, foi proposta uma abordagem baseada no ajuste do modelo GARMA. / In public health several approaches have been proposed for the detection of outbreaks of infectious diseases where the characteristic being monitored is a time series of count data as the number of hospitalizations, where the population and the expected rate of admissions change over time. In this work we fitted generalized linear models (GLM) and implemented Shewhart and CUSUM control charts for monitoring the daily number of hospital admissions due to respiratory diseases for people aged 65 and older in the city of São Paulo. Through simulations, we evaluated the efficiency of implementing five different statistical for detecting changes in time series of count. One approach consisted of applying three transformations that only depend on the parameters of the negative binomial and Poisson distributions: The transformations of Rossi for data with Poisson distribution, the transformation proposed by Jorgensen for data with negative binomial distribution and residuals proposed by Anscombe for generalized linear models. The other statistics have been proposed as CUSUM charts: the method of Rogerson e Yamada (2004) was presented for data with Poisson distribution, in this work we proposed a new parameter kt for negative binomial distribution, the proposed method for Hohle (2007) uses the likelihood ratio statistic. Implementing limit control assuming normality for a value of ARL0 = 500 be monitored via simulation the serie of interest implementing the normalizing statistics. However, using these limits was observed a greater number of alarms for the three transformations. Modifying the parameter k of the CUSUM chart to this change over time the series was monitored and were obtained values of ARL0 close to 500. The CUSUM control charts for the methods of Rogerson and Yamada and Holhe for data with negative binomial distribution showed, by simulation, good results for detecting changes in the mean. For negative binomial distribution generalizing the method of Rogerson e Yamada (2004) and implemented the CUSUM charts using the likelihood ratio statistic. Both methods provided good results via simulation to detect small changes in average. The evaluation of assumptions of normality for the statistics proposed by Rossi, Jorgensen and Anscombe generally is omitted in published studies. In this work, we evaluated this assumptions indicating that the statistics are not normal using the real dataset but are independent and stationary. By analyzing real data, due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent. Due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent.
4

Estimation du maximum de vraisemblance dans les modèles de Markov partiellement observés avec des applications aux séries temporelles de comptage / Maximum likelihood estimation in partially observed Markov models with applications to time series of counts

Sim, Tepmony 08 March 2016 (has links)
L'estimation du maximum de vraisemblance est une méthode répandue pour l'identification d'un modèle paramétré de série temporelle à partir d'un échantillon d'observations. Dans le cadre de modèles bien spécifiés, il est primordial d'obtenir la consistance de l'estimateur, à savoir sa convergence vers le vrai paramètre lorsque la taille de l'échantillon d'observations tend vers l'infini. Pour beaucoup de modèles de séries temporelles, par exemple les modèles de Markov cachés ou « hidden Markov models »(HMM), la propriété de consistance « forte » peut cependant être dfficile à établir. On peut alors s'intéresser à la consistance de l'estimateur du maximum de vraisemblance (EMV) dans un sens faible, c'est-à-dire que lorsque la taille de l'échantillon tend vers l'infini, l'EMV converge vers un ensemble de paramètres qui s'associent tous à la même distribution de probabilité des observations que celle du vrai paramètre. La consistance dans ce sens, qui reste une propriété privilégiée dans beaucoup d'applications de séries temporelles, est dénommée consistance de classe d'équivalence. L'obtention de la consistance de classe d'équivalence exige en général deux étapes importantes : 1) montrer que l'EMV converge vers l'ensemble qui maximise la log-vraisemblance normalisée asymptotique ; et 2) montrer que chaque paramètre dans cet ensemble produit la même distribution du processus d'observation que celle du vrai paramètre. Cette thèse a pour objet principal d'établir la consistance de classe d'équivalence des modèles de Markov partiellement observés, ou « partially observed Markov models » (PMM), comme les HMM et les modèles « observation-driven » (ODM). / Maximum likelihood estimation is a widespread method for identifying a parametrized model of a time series from a sample of observations. Under the framework of well-specified models, it is of prime interest to obtain consistency of the estimator, that is, its convergence to the true parameter as the sample size of the observations goes to infinity. For many time series models, for instance hidden Markov models (HMMs), such a “strong” consistency property can however be difficult to establish. Alternatively, one can show that the maximum likelihood estimator (MLE) is consistent in a weakened sense, that is, as the sample size goes to infinity, the MLE eventually converges to a set of parameters, all of which associate to the same probability distribution of the observations as for the true one. The consistency in this sense, which remains a preferred property in many time series applications, is referred to as equivalence-class consistency. The task of deriving such a property generally involves two important steps: 1) show that the MLE converges to the maximizing set of the asymptotic normalized loglikelihood; and 2) show that any parameter in this maximizing set yields the same distribution of the observation process as for the true parameter. In this thesis, our primary attention is to establish the equivalence-class consistency for time series models that belong to the class of partially observed Markov models (PMMs) such as HMMs and observation-driven models (ODMs).
5

Contribution à l'économétrie des séries temporelles à valeurs entières / Contribution to econometrics of time series with integer values

Ahmad, Ali 05 December 2016 (has links)
Dans cette thèse, nous étudions des modèles de moyennes conditionnelles de séries temporelles à valeurs entières. Tout d’abord, nous proposons l’estimateur de quasi maximum de vraisemblance de Poisson (EQMVP) pour les paramètres de la moyenne conditionnelle. Nous montrons que, sous des conditions générales de régularité, cet estimateur est consistant et asymptotiquement normal pour une grande classe de modèles. Étant donné que les paramètres de la moyenne conditionnelle de certains modèles sont positivement contraints, comme par exemple dans les modèles INAR (INteger-valued AutoRegressive) et les modèles INGARCH (INteger-valued Generalized AutoRegressive Conditional Heteroscedastic), nous étudions la distribution asymptotique de l’EQMVP lorsque le paramètre est sur le bord de l’espace des paramètres. En tenant compte de cette dernière situation, nous déduisons deux versions modifiées du test de Wald pour la significativité des paramètres et pour la moyenne conditionnelle constante. Par la suite, nous accordons une attention particulière au problème de validation des modèles des séries temporelles à valeurs entières en proposant un test portmanteau pour l’adéquation de l’ajustement. Nous dérivons la distribution jointe de l’EQMVP et des autocovariances résiduelles empiriques. Puis, nous déduisons la distribution asymptotique des autocovariances résiduelles estimées, et aussi la statistique du test. Enfin, nous proposons l’EQMVP pour estimer équation-par-équation (EpE) les paramètres de la moyenne conditionnelle des séries temporelles multivariées à valeurs entières. Nous présentons les hypothèses de régularité sous lesquelles l’EQMVP-EpE est consistant et asymptotiquement normal, et appliquons les résultats obtenus à plusieurs modèles des séries temporelles multivariées à valeurs entières. / The framework of this PhD dissertation is the conditional mean count time seriesmodels. We propose the Poisson quasi-maximum likelihood estimator (PQMLE) for the conditional mean parameters. We show that, under quite general regularityconditions, this estimator is consistent and asymptotically normal for a wide classeof count time series models. Since the conditional mean parameters of some modelsare positively constrained, as, for example, in the integer-valued autoregressive (INAR) and in the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH), we study the asymptotic distribution of this estimator when the parameter lies at the boundary of the parameter space. We deduce a Waldtype test for the significance of the parameters and another Wald-type test for the constance of the conditional mean. Subsequently, we propose a robust and general goodness-of-fit test for the count time series models. We derive the joint distribution of the PQMLE and of the empirical residual autocovariances. Then, we deduce the asymptotic distribution of the estimated residual autocovariances and also of a portmanteau test. Finally, we propose the PQMLE for estimating, equation-by-equation (EbE), the conditional mean parameters of a multivariate time series of counts. By using slightly different assumptions from those given for PQMLE, we show the consistency and the asymptotic normality of this estimator for a considerable variety of multivariate count time series models.

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