A distribuição beta é a mais frequentemente utilizada para a modelagem de dados contínuos observados no intervalo unitário, como taxas e proporções. Embora seja flexível, admitindo formas variadas, tais como J, J invertido, U e unimodal, não é adequada em todas as situações práticas. Nesta dissertação fazemos uma revisão sobre distribuições contínuas no intervalo unitário englobando as distribuições beta, Kumaraswamy, simplex, gama unitária e beta retangular. Também abordamos uma ampla classe de distribuições obtida por transformações (Smithson e Merkle, 2013). Em particular, focamos em duas subclasses, uma apresentada e estudada por Lemonte e Bazán (2015), que chamaremos de classe de distribuições logito, e outra que chamaremos de classe de distribuições logito skew. Todas as distribuições consideradas são aplicadas a conjuntos de dados do Banco Mundial. / The beta distribution is the most frequently used for modeling continuous data observed in the unit interval, such as rates and proportions. Although flexible, assuming varied forms, such as J, inverted J, U and unimodal, it is not suitable in all practical situations. In this dissertation we make a review on continuous distributions in the unit interval encompassing the beta, Kumaraswamy, simplex, unit gamma and rectangular beta distributions. We also address a wide class of distributions obtained by transformations (Smithson and Merkle, 2013). In particular, we focus on two subclasses, one presented and studied by Lemonte and Bazán (2015), which we will call the logit class of distributions, and another that we will call the logit class of skew distributions. All distributions considered are applied to World Bank data sets.

A distribuição beta é a mais frequentemente utilizada para a modelagem de dados contínuos observados no intervalo unitário, como taxas e proporções. Embora seja flexível, admitindo formas variadas, tais como J, J invertido, U e unimodal, não é adequada em todas as situações práticas. Nesta dissertação fazemos uma revisão sobre distribuições contínuas no intervalo unitário englobando as distribuições beta, Kumaraswamy, simplex, gama unitária e beta retangular. Também abordamos uma ampla classe de distribuições obtida por transformações (Smithson e Merkle, 2013). Em particular, focamos em duas subclasses, uma apresentada e estudada por Lemonte e Bazán (2015), que chamaremos de classe de distribuições logito, e outra que chamaremos de classe de distribuições logito skew. Todas as distribuições consideradas são aplicadas a conjuntos de dados do Banco Mundial. / The beta distribution is the most frequently used for modeling continuous data observed in the unit interval, such as rates and proportions. Although flexible, assuming varied forms, such as J, inverted J, U and unimodal, it is not suitable in all practical situations. In this dissertation we make a review on continuous distributions in the unit interval encompassing the beta, Kumaraswamy, simplex, unit gamma and rectangular beta distributions. We also address a wide class of distributions obtained by transformations (Smithson and Merkle, 2013). In particular, we focus on two subclasses, one presented and studied by Lemonte and Bazán (2015), which we will call the logit class of distributions, and another that we will call the logit class of skew distributions. All distributions considered are applied to World Bank data sets.

Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.

We use inequalities to design short universal algorithms that can be used to generate random variates from large classes of univariate continuous or discrete distributions (including all log-concave distributions). The expected time is uniformly bounded over all these distributions. The algorithms can be implemented in a few lines of high level language code. In opposition to other black-box algorithms hardly any setup step is required and thus it is superior in the changing parameter case. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

In this paper we describe a version of transformed density rejection that requires less uniform random numbers. Random variates below the squeeze are generated by inversion. For the expensive part between squeeze and density an algorithm that uses a coverering with triangles is introduced. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

We use inequalities to design short universal algorithms that can be used to generate random variates from large classes of univariate continuous or discrete distributions (including all log-concave distributions). The expected time is uniformly bounded over all these distributions for a particular generator. The algorithms can be implemented in a few lines of high level language code. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Η παρούσα διπλωματική εργασία αντλεί την θεματολογία της από την θεωρία ελέγχων καλής προσαρμογής. Δίνονται τα βασικά σημεία της θεωρίας ελεγχοσυναρτήσεων και στη συνέχεια παρουσιάζεται η επέκταση του έλεγχου των Kolmogorov-Smirnov στο διδιάστατο χώρο καθώς και μια τροποποίησή της. Βασικό βοήθημα για την επέκταση αυτή αποτελεί το θεώρημα του Rosenblatt, το οποίο προτείνει ένα μετασχηματισμό μιας απόλυτα συνεχούς k-διάστατης κατανομής σε ομοιόμορφη κατανομή στον k-διάστατο υπερκύβο. Παρουσιάζεται επίσης το στατιστικό Α, το οποίο προτάθηκε από τον Damico. Η ιδιαιτερότητα αυτού του στατιστικού είναι ότι έχει διακριτή κατανομή. Προτείνεται ένα στατιστικό για τον έλεγχο καλής προσαρμογής συνεχών δεδομένων αρχικά στις δύο και στη συνέχεια στις k διαστάσεις. Ως εργαλεία χρησιμοποιήθηκαν το στατιστικό Α και το Θεώρημα του Rosenblatt. Για διάφορα μεγέθη δείγματος, δίνονται ο πίνακας πιθανοτήτων για τις τιμές του στατιστικού καθώς και ο πίνακας με τις κρίσιμες τιμές για διάφορες τιμές του p-value. Οι πίνακες αυτοί προέκυψαν κυρίως με μεθόδους προσομοίωσης. Τέλος, υπολογίστηκε η ισχύς του ελέγχου και γίνεται σύγκριση με την ισχύ του διδιάστατου Kolmogorov-Smirnov. / This project is based in theory of goodness-fit-tests. We present the most important componenets of test funcion theory. Also, we present the extension of the Kolmogorov-Smirnov test in bivariate case and an approximation. This extension is based on Rosenblatt's theorem, which suggests a transformation of an absolutly continious k-variate distribution into the uniform distribution of the k-dimentional hypercube. Moreover, is presented the statistic A, which was suggested from Damico. The particularity of this statistic is that has a district contribution. We suggest a goodnes-of-fit test for continious data first on two dimensions and after on k dimensions. This new statistic uses Rosenblatt's transformation and the statistic A. For different sizes of sample, are given the table of probablities and the table with the critical values. These tables were arised with simulation methods. Finally, was computed the power of the test and was compared with the power of the bivariate Kolmogorv-Smirnov.

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-08-03T20:04:27Z No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:23Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:30Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Made available in DSpace on 2017-08-08T19:15:39Z (GMT). No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) Previous issue date: 2016-12-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.

This thesis contains theory of probability and statistical sets. Solved and unsolved problems of probability, random variable and distributions random variable, random vector, statistical sets, regression and correlation analysis. Unsolved problems contains solutions.

Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.