There are 506 announcements of stock repurchases from 1999.8.9 to the end of 2001 after Taiwan adopting the law of treasury stock. It¡¦s obviously that companies issued in the stock market need the law because the percent of applying is up to 37.23%. We study the announcements during 1999.8.9 to 2001.12.31 and try to find out the motives of stock repurchases. For understanding the effect of market prediction, we try to set up a prediction model and separate the result of market prediction to right and wrong. Regarding the factor of affecting the cumulative abnormal return after announcement, we argue that it¡¦s quite similar with cash dividend announcement as companies signal the good news of becoming better in the future. It infers that the effect of announcement relates to former accounting information. The result show as following: (1) the motives to stock repurchases are consistent with optimal leverage ratio hypothesis, dividend or tax hypothesis. The companies would intend to stock repurchase when the board of directors had higher collateral ratio or the enterprise ever used the subsidiary company to repurchase the stock. (2) we can¡¦t prove that the unexpected announcement has higher abnormal return than the expected one. (3) we also can¡¦t prove that the former accounting information affect the abnormal return, but we can see the it positively relates to the free cash flow, undervaluation, and the degree of information asymmetry.

Draper and Guttman (1997) show that for basic 2^{k-p}designs, p >= 0, k-p replicates of blocks designs of size two are needed to estimate all the usual (estimable) effects. In Chapter 1, we provide an algebraic formal proof for the two-level blocks designs results and present results applicable to the general case; that is, for the case of s^{k} factorial (p=0) or s^{k-p}ractional factorial (p>0) designs in s^{b} blocks, where 0<b<k-p, at least (k-p)/(k-p-b) replicates are needed to clear up all possible effects. Through the theoretical development presented in this work, it can provide a clearer view on why those results would hold. We will also discuss the estimation equations given in Draper and Guttman (1997). In Chapter 2, we present a methodology for analyzing the variability of the radiated emission testings of electronic, elecommunication and information technology equipment based on a modified analysis of variance (ANOVA), with polynomial regression analysis. In our study, three electronic products; modem, monitor and notebook bought from the market are tested. Through the experiment, we show that the international standard fails to provide a methodology which gives control limits for EMC when the electronic products in question are produced. We feel that an improved EMC control procedure presented here can better meet the needs of radiated emission control.

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-11-16Bitstream added on 2014-06-13T21:02:57Z : No. of bitstreams: 1 barbosa_l_dr_botfca.pdf: 2587351 bytes, checksum: 76e343f1e0edbbbee5cb996188d8efd2 (MD5) / É bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados / Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data

Orientador: Luiza Aparecida Trinca / Banca: Liciana Vaz da Arruda / Banca: Osmar Delmanto Junior / Banca: Célia Regina Lopes Zimback / Banca: Marli Teixeira de A. Minhoni / Resumo: É bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados / Abstract: Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data / Doutor

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L'objectif de cette thèse était de faire un bilan des méthodes et outils validés chez les patients DT2 et de développer une méthode d'estimation de la DE précise et adaptée à ces patients. Après avoir défini les activités quotidiennes les plus couramment réalisées dans une population de patients DT2 (Étude 1), nous nous sommes intéressés aux activités statiques et à la marche. Notre seconde étude a montré que le coût énergétique brut et net lors de la marche est respectivement 8,7 et 14,3 % plus élevé chez les patients DT2 en comparaison à un groupe témoin. Ce résultat mettait en avant la nécessité de valider une méthode d'estimation de la DE adaptée aux spécificités de la population diabétique. Les résultats de notre troisième étude ont démontré la validité de la DE estimée à partir d'un capteur intelligent et d'un algorithme existant pour différentes positions statiques et vitesses de marche chez des sujets DT2 et témoins. Enfin, dans une quatrième étude, trois équations d'estimation de la DE totale ont été développées à partir des données d'accélérations issues de trois capteurs intelligents (bas du dos, hanche et cheville) pour une population de sujets DT2 et témoins. Ces trois équations permettent d'estimer une DE totale lors d'activités statiques et de marches avec une différence moyenne respectivement de 0,009, -0,007 et -0,008 kcal/min pour le bas du dos, la hanche et la cheville en comparaison à celle de référence. Ainsi, ce travail de thèse a permis de valider trois équations d’estimation de la DE totale adaptés à une population de patients DT2, normo-pondérés ou en surpoids et âgés de 35 à 80 ans, basés sur les données d'un capteur intelligent. / The aim of this thesis is to review the methods and tools validated in T2D patients and to developed precise and adapted EE estimation method for these patients. After defining the most common daily activity in T2D patients (Study 1), we are interested, in particular, on static activities and walking. The results of our second study showed that the gross and net metabolic rate were 8.7 % and 14.3 % higher in T2D patients than in control subjects, respectively. These results highlight the need to validate an EE estimation method, adapted to diabetic’s specificities. The results of our third study demonstrated the validity of the activity-related EE estimated with a smart sensor and a validated algorithm during static activities and walking in T2D patients and healthy subjects. However, our results showed a lower accuracy of the estimated EE during walking in T2D patients in comparison with healthy subjects. Finally, in a fourth study, three equations estimating the total EE have been developed with acceleration data from three smart sensors (lower back, hip and ankle) and personal data (body mass index, sex, diabetes) from T2D and healthy subjects. These three equations allow to estimate the total EE during static activities and walking with a mean bias of 0.009, -0.007 et -0.008 kcal/min for the lower back, hip and ankle equation, respectively, in comparison with the reference value. Thus, this thesis enables to validate three suitable equations for the estimation of total EE in population including healthy and T2D patients, normal-weighted and overweighed and aged between 35 and 80 years, based on data from a smart sensor.

Made available in DSpace on 2016-06-02T20:06:07Z (GMT). No. of bitstreams: 1 5034.pdf: 832896 bytes, checksum: 2e9dd202302339e95fd416a410d6eb7e (MD5) Previous issue date: 2013-03-11 / Financiadora de Estudos e Projetos / One of the distributions used to model extremal events is the type I extremevalue distribution (Gumbel distribution). The usual extreme-value regression model requires independent observations. In this work, using generalized linear model (Mc-Cullagh e Nelder, 1989) and generalized estimating equations (Liang e Zeger, 1986), we developed the extreme-value regression model when there are independent clusters formed by dependent variables. The behavior of parameter estimators of the proposed model is studied through Monte Carlo simulations. / A distribuição valor extremo tipo I, também conhecida como distribuição Gumbel, é uma das distribuições utilizadas para a modelagem de eventos extremos. Os modelos existentes de regressão valor extremo supõem que as observações sejam independentes, inviabilizando o uso desses modelos quando existe dependência entre as observações. Nesta dissertação, utilizando modelos lineares generalizados (McCullagh e Nelder, 1989) e equações de estimação generalizadas (Liang e Zeger, 1986), desenvolvemos o modelo de regress~ao valor extremo para o caso em que h a grupos independentes formados por respostas dependentes. O comportamento dos estimadoresdos parâmetros do modelo proposto é avaliada através de simulações Monte Carlo.

This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.