China's economic development has performed spectacularly during the period of China's economic transition as a result of radical economic reform in the all markets. The country has also gone through extensive fiscal reforms in the last three decades. However, a number of problems have been associated with such rapid economic growth. One of these has been raising inequality. In both Keynesian and neoclassical endogenous growth theories, public spending can play an important role for economic growth and inequality. The majority of previous studies have focused on the relationship between public spending and economic growth, or between public spending and inequality separately. There is no doubt that public spending has an effect on both economic growth and equity simultaneously. In this respect, this thesis attempts to address the problems that have emerged during the period of China's fiscal reforms, and seeks to examine the effects of public spending on economic growth and equality in the same model. This thesis investigates the dynamic relationships among these three variables in China. For aggregate national data, vector error correction model (VECM) has been used. Analysis at the provincial level is based on the panel vector auto-regression (PVAR) model. These methods help to solve the endogeneity in estimations. The national level analysis indicates that total public spending shows a long term Granger causality with GDP per capita, which supports the positive growth effect of public spending in the Keynesian and endogenous growth model. Social public spending has a negative effect on real output per capita in both the short term and long term, but it also has a negative impact on income inequality. Moreover, we find that a higher level of real GDP per capita will increase the level of inequality, but a higher level of inequality has a negative effect on real GDP per capita in the long term. Furthermore, total provincial public spending and provincial social spending have either a non-significant effect on economic growth. On the other hand, the SOEs' investment has a significant, positive growth effect at both the national and provincial level. As for the redistributive role of the public spending, the provincial total public spending and social spending have played an important role on income distribution. Furthermore, the Gini coefficient has a positive effect on the per capita growth rate at the provincial level, but the economic growth has no significant impact on the Gini coefficient.

This thesis investigates which factors affecting savings behavior by using a fixed effect regression model. To see what affects the household savings rate the following independent variables is considered: Natural logarithm of trend per capita income, natural logarithm of deviation from trend per capita income, growth of disposable income, real interest rate, inflation, wealth in relation to household disposable income, foreign savings in relation to disposable income, dependency ratio and human development index. To see whether changes of human development within a county impacts the household´s savings ratio this variables was included in a separate regression. To avoid possible biasedness from ordinary least square, a panel data technique called fixed effect regression model is used. The investigated time period is between year 1999 and 2016 and to make a restriction, variables from 25 developed countries were studied. The involved economic theories in this work are Keynesianism, permanent income hypothesis and the savings theory behind Maslow´s behavioral pyramid. The result made by using this study is that growth in income and foreign savings in relation to disposable income is insignificant and can´t be used in explaining the differences between household´s savings. Human development index within a country has a negative effect on the savings ratio but a conclusion regarding whether changes in HDI´s does affect savings can´t be made and more research within that field is needed.

This study proposes a social and emotional disclosure model for understanding the mechanism that explains sharing intimate information on social media (Twitter). Previous research has indicated that some aspects of social, emotional, and attitudinal information processing are involved in disclosure of intimate information. However, these factors have been considered in isolation. This study proposes and tests a theoretically grounded model that brings all of these factors together by combining individual and group social media behaviors and online information processing in the realm of online social movements. The core explanatory model considers the impact of peer response, emotional evaluation, personal relevance, issue orientation, and motivation to post online on intimate information disclosure online. A path analysis building on four Poisson multiple regressions conducted on 28,629 #MeToo tweets evaluates the relationships proposed in the explanatory model. Results indicate that emotional evaluation and motivation to post online have direct, positive impacts on online disclosure. Other factors such as peer response, issue orientation, and personal relevance have negative direct relationships with online disclosure. Motivation to post online mediates the effects of emotional evaluation, issue orientation, and personal relevance on online disclosure while issue orientation mediates the effect of personal relevance on motivation to post online. This study offers findings that have use for practitioners interested in hashtag virality and to social media users interested in social influence and online information sharing.

The purpose of this thesis is to investigate further whether oil price has an effect on house price inflation and additionally if it has a link to house price turning points. The methodology is grounded on the previous research paper made by Breitenfellner et al. (2015). The results are based on quarterly data from the countries; Finland, Denmark, Norway and Sweden through the time span of 1990-2018. A linear fixed regression model was performed including the explanatory variables of monetary policy and credit developments, macroeconomic fundamentals, housing market variable and demographic variables. Secondly, a logit model was used to identify a relationship between oil price and house price turning points. The model used misalignment made from GDP per capita and real interest rate. The empirical analysis confirms that there is a positive relationship between oil prices and house price inflation. This evidence contradicts a major share of previous research papers (see Bernanke, 2010; Kaufmann et al., 2011). However, there are also some previous papers (see Yiqi, (2017); Antonakakis et al., 2016) and theoretical linkages in line with a positive correlation. Concerning, the oil price and house price inflation no empirical significance was found regarding their relationship. For future research, one could include regional aspects for the purpose of controlling for geographical differences.

O modelo semiparamétrico de Cox é frequentemente utilizado na modelagem de dados de sobrevivência, pois é um modelo muito flexível e permite avaliar o efeito das covariáveis sobre a taxa de falha. Uma das principais vantagens é a fácil interpretação, de modo que a razão de riscos de dois indivíduos não varia ao longo do tempo. No entanto, em algumas situações a proporcionalidade dos riscos para uma dada covariável pode não ser válida e, este caso, uma abordagem que não dependa de tal suposição é necessária. Nesta tese, propomos um modelo tipo Cox em que o efeito da covariável e a função de risco basal são representadas via bases de Fourier e ondaletas de Haar clássicas e deformadas. Propomos também um procedimento de predição da função de sobrevivência para um paciente específico. Estudos de simulações e aplicações a dados reais sugerem que nosso método pode ser uma ferramenta valiosa em situações práticas em que o efeito da covariável é dependente do tempo. Por meio destes estudos, fazemos comparações entre as duas abordagens propostas, e comparações com outra já conhecida na literatura, onde verificamos resultados satisfatórios. / The semiparametric Cox model is often considered when modeling survival data. It is very flexible, allowing for the evaluation of covariates effects. One of its main advantages is the easy of interpretation, as long as the rate of the hazards for two individuals does not vary over time. However, this proportionality of the hazards may not be true in some practical situations and, in this case, an approach not relying on such assumption is needed. In this thesis we propose a Cox-type model that allows for time-varying covariate effects, for which the baseline hazard is based on Fourier series and wavelets on a time-frequency representation. We derive a prediction method for the survival of future patients with any specific set of covariates. Simulations and an application to a real data set suggest that our method may be a valuable tool to model data in practical situations where covariate effects vary over time. Through these studies, we make comparisons between the two approaches proposed here and comparisons with other already known in the literature, where we verify satisfactory results.

The Beta distribution is a bounded domain distribution which has dominated the modeling the distribution of random variable that assume value between 0 and 1. Bounded domain distributions arising in various situations such as rates, proportions and index. Motivated by an analysis of electoral votes percentages (where a distribution with support on the positive real numbers was used, although a distribution with limited support could be more suitable) we focus on alternative distributions to Beta distribution with emphasis in regression models. In this work, initially we present the Simplex mixture model as a flexible model to modeling the distribution of bounded random variable then we extend the model to the context of regression models with the inclusion of covariates. The parameters estimation is discussed for both models considering Bayesian inference. We apply these models to simulated data sets in order to investigate the performance of the estimators. The results obtained were satisfactory for all the cases investigated. Finally, we introduce a parameterization of the L-Logistic distribution to be used in the context of regression models and we extend it to a mixture of mixed models. / A distribuição beta é uma distribuição com suporte limitado que tem dominado a modelagem de variáveis aleatórias que assumem valores entre 0 e 1. Distribuições com suporte limitado surgem em várias situações como em taxas, proporções e índices. Motivados por uma análise de porcentagens de votos eleitorais, em que foi assumida uma distribuição com suporte nos números reais positivos quando uma distribuição com suporte limitado seira mais apropriada, focamos em modelos alternativos a distribuição beta com enfase em modelos de regressão. Neste trabalho, apresentamos, inicialmente, um modelo de mistura de distribuições Simplex como um modelo flexível para modelar a distribuição de variáveis aleatórias que assumem valores em um intervalo limitado, em seguida estendemos o modelo para o contexto de modelos de regressão com a inclusão de covariáveis. A estimação dos parâmetros foi discutida para ambos os modelos, considerando o método bayesiano. Aplicamos os dois modelos a dados simulados para investigarmos a performance dos estimadores usados. Os resultados obtidos foram satisfatórios para todos os casos investigados. Finalmente, introduzimos a distribuição L-Logistica no contexto de modelos de regressão e posteriormente estendemos este modelo para o contexto de misturas de modelos de regressão mista.

Die Dissertation ``Asymptotische \Äquivalenz f\ür ein Modell unabh\ängiger nicht identisch verteilter Daten'' besch\äftigt sich mit der Le Camschen Theorie der Experimente. Le Cam hat den sogenannten $\Delta$-Abstand zwischen statistischen Experimenten definiert; ist dieser Abstand f\ür zwei Modelle klein, so sind ihre statistischen Eigenschaften \ähnlich. Zwei Folgen von Experimenten nennt man asymptotisch \äquivalent, falls ihr $\Delta$-Abstand gegen Null konvergiert.\\ In dieser Arbeit beweisen wir asymptotische \Äquivalenz zwischen einem Modell mit unabh\ängigen, nicht identisch verteilten Beobachtungen und einem Gaußschen Shift-Modell. Die i-te Beobachtung des ersten Experimentes ist dabei gem\äß einer Dichte $h(i/n,.)$ verteilt, wobei die Funktion h eine Schar von Dichten bildet. Wir approximieren also ein kompliziertes statistisches Experiment durch ein einfacheres, n\äymlich ein Gaußsches Shift-Modell. Die Dichten h geh\ören einer Menge h\ölderstetiger Funktionen an, so daß wir es mit einem nichtparametrischen Problem zu tun haben. Das von uns bewiesene \Äquivalenzresultat kann auch als eine nichtparametrische Version der ebenfalls von Le Cam eingef\ührten LAN Bedingung aufgefaßt werden. Ein wichtiges Hilfsmittel zum Beweis des oben beschriebenen Resultats ist das sogenannte Coupling von stochastischen Prozessen, d.h. die Konstruktion solcher Prozesse auf einem gemeinsamen Wahrscheinlichkeitsraum, so daß die Prozesse nahe beieinander liegen. Im zweiten Teil der Arbeit beweisen wir eine funktionale Version eines solchen Coupling Resultats f\ür den sequentiellen empirischen Prozeß und den Kiefer-M\üller Prozeß unter Verwendung der sogenannten Ungarischen Konstruktion. / The thesis "Asymptotic Equivalence of Experiments for a Model with Independent and Nonidentically distributed Observations" deals with the theory of experiments that was developped by Le Cam. \\ Le Cam defined the so called $\Delta$-distance between experiments. If this distance is small for two given models it means that their statistical properties are similar. We call two sequences of experiments asymptotic equivalent if their $\Delta$-distance converges to zero.\\ In this thesis we prove asymptotic equivalence between a model with independent and nonidentically distributed observations and a Gaussian shift model. The i-th observation in the first model is distributed according to a density $h(i/n,.)$ where $h$ is a bunch of densities on the unit interval. This means that we approximate a complicated statistical experiment by a simpler one, namely a Gaussian shift model. The densites h belong to a H\"older ball such that we have a nonparametric problem. Our result can also be viewed as a nonparametric version of the LAN property which was also defined by Le Cam. An important tool for proving our result is the coupling of stochastic processes, i.e. the construction of processes on a common probability space such that they are close in a strong sense. In the second part of the thesis we prove a functional version of such a coupling result for the sequential empirical process and the Kiefer-M\"uller process by using the Hungarian construction.

La reconnaissance d’activités humaines est un sujet de recherche d’actualité comme en témoignent les nombreux travaux de recherche sur le sujet. Dans ce cadre, la reconnaissance des activités physiques humaines est un domaine émergent avec de nombreuses retombées attendues dans la gestion de l’état de santé des personnes et de certaines maladies, les systèmes de rééducation, etc.Cette thèse vise la proposition d’une approche pour la reconnaissance automatique et non-intrusive d’activités physiques quotidiennes, à travers des capteurs inertiels de type accéléromètres, placés au niveau de certains points clés du corps humain. Les approches de reconnaissance d’activités physiques étudiées dans cette thèse, sont catégorisées en deux parties : la première traite des approches supervisées et la seconde étudie les approches non-supervisées. L’accent est mis plus particulièrement sur les approches non-supervisées ne nécessitant aucune labellisation des données. Ainsi, nous proposons une approche probabiliste pour la modélisation des séries temporelles associées aux données accélérométriques, basée sur un modèle de régression dynamique régi par une chaine de Markov cachée. En considérant les séquences d’accélérations issues de plusieurs capteurs comme des séries temporelles multidimensionnelles, la reconnaissance d’activités humaines se ramène à un problème de segmentation jointe de séries temporelles multidimensionnelles où chaque segment est associé à une activité. L’approche proposée prend en compte l’aspect séquentiel et l’évolution temporelle des données. Les résultats obtenus montrent clairement la supériorité de l’approche proposée par rapport aux autres approches en termes de précision de classification aussi bien des activités statiques et dynamiques, que des transitions entre activités. / Human activity recognition is currently a challengeable research topic as it can be witnessed by the extensive research works that has been conducted recently on this subject. In this context, recognition of physical human activities is an emerging domain with expected impacts in the monitoring of some pathologies and people health status, rehabilitation procedures, etc. In this thesis, we propose a new approach for the automatic recognition of human activity from raw acceleration data measured using inertial wearable sensors placed at key points of the human body. Approaches studied in this thesis are categorized into two parts : the first one deals with supervised-based approaches while the second one treats the unsupervised-based ones. The proposed unsupervised approach is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context where each segment is associated with an activity. The model is learned in an unsupervised framework where no activity labels are needed. The proposed approach takes into account the sequential appearance and temporal evolution of data. The results clearly show the satisfactory results of the proposed approach with respect to other approaches in terms of classification accuracy for static, dynamic and transitional human activities

A classe de modelos de regressão beta tem sido estudada amplamente. Porém, para esta classe de modelos existem poucos trabalhos sobre a inclusão de efeitos aleatórios e a flexibilização da distribuição dos efeitos aleatórios, além de métodos de predição e de diagnóstico no ponto de vista dos efeitos aleatórios. Neste trabalho são propostos modelos de regressão beta com efeitos aleatórios normais e não normais para dados longitudinais. Os métodos de estimação de parâmetros e de predição dos efeitos aleatórios usados no trabalho são o método de máxima verossimilhança e o método do melhor preditor de Bayes empírico. Para aproximar a função de verossimilhança foi utilizada a quadratura de Gauss-Hermite. Métodos de seleção de modelos e análise de resíduos também foram propostos. Foi implementado o pacote BLMM no R para a realização de todos os procedimentos. O processo de estimação os parâmetros dos modelos e a distribuição empírica dos resíduos propostos foram analisados por meio de estudos de simulação. Foram consideradas várias distribuições para os efeitos aleatórios, valores para o número de indivíduos, número de observações por indivíduo e estruturas de variância-covariância para os efeitos aleatórios. Os resultados dos estudos de simulação mostraram que o processo de estimação obtém melhores resultados quando o número de indivíduos e o número de observações por indivíduo aumenta. Estes estudos também mostraram que o resíduo quantil aleatorizado segue uma distribuição aproximadamente normal. A metodologia apresentada é uma ferramenta completa para analisar dados longitudinais contínuos que estão restritos ao intervalo limitado (0; 1). / The class of beta regression models has been studied extensively. However, there are few studies on the inclusion of random effects and models with flexible random effects distributions besides prediction and diagnostic methods. In this work we proposed a beta regression models with normal and not normal random effects for longitudinal data. The maximum likelihood method and the empirical Bayes approach are used to obtain the estimates and the best prediction. Also, the Gauss-Hermite quadrature is used to approximate the likelihood function. Model selection methods and residual analysis were also proposed.We implemented a BLMM package in R to perform all procedures. The estimation procedure and the empirical distribution of residuals were analyzed through simulation studies considering differents random effects distributions, values for the number of individuals, number of observations per individual and covariance structures for the random effects. The results of simulation studies showed that the estimation procedure obtain better results when the number of individuals and the number of observations per individual increase. These studies also showed that the empirical distribution of the quantile randomized residual follows a normal distribution. The methodolgy presented is a tool for analyzing longitudinal data restricted to a interval (0; 1).

In this study we propose three new distributions and a study with longitudinal data. The first was the Odd log-logistic normal distribution: theory and applications in analysis of experiments, the second was Odd log-logistic t Student: theory and applications, the third was the Odd log-logistic skew normal: the new distribution skew-bimodal with applications in analysis of experiments and the fourth regression model with random effect of the Odd log-logistic skew normal distribution: an application in longitudinal data. Some have been demonstrated such as symmetry, quantile function, some expansions, ordinary incomplete moments, mean deviation and the moment generating function. The estimation of the model parameters were approached by the method of maximum likelihood. In applications were used regression models to data from a completely randomized design (CRD) or designs completely randomized in blocks (DBC). Thus, the models can be used in practical situations for as a completely randomized designs or completely randomized blocks designs, mainly, with evidence of asymmetry, kurtosis and bimodality. / A distribuição normal é uma das mais importantes na área de estatística. Porém, não é adequada para ajustar dados que apresentam características de assimetria ou de bimodalidade, uma vez que tal distribuição possui apenas os dois primeiros momentos, diferentes de zero, ou seja, a média e o desvio-padrão. Por isso, muitos estudos são realizados com a finalidade de criar novas famílias de distribuições que possam modelar ou a assimetria ou a curtose ou a bimodalidade dos dados. Neste sentido, é importante que estas novas distribuições tenham boas propriedades matemáticas e, também, a distribuição normal como um submodelo. Porém, ainda, são poucas as classes de distribuições que incluem a distribuição normal como um modelo encaixado. Dentre essas propostas destacam-se: a skew-normal, a beta-normal, a Kumarassuamy-normal e a gama-normal. Em 2013 foi proposta a nova família X de distribuições Odd log-logística-G com o objetivo de criar novas distribuições de probabildade. Assim, utilizando as distribuições normal e a skew-normal como função base foram propostas três novas distribuições e um quarto estudo com dados longitudinais. A primeira, foi a distribuição Odd log-logística normal: teoria e aplicações em dados de ensaios experimentais; a segunda foi a distribuição Odd log-logística t Student: teoria e aplicações; a terceira foi a distribuição Odd log-logística skew-bimodal com aplicações em dados de ensaios experimentais e o quarto estudo foi o modelo de regressão com efeito aleatório para a distribuição distribuição Odd log-logística skew-bimodal: uma aplicação em dados longitudinais. Estas distribuições apresentam boas propriedades tais como: assimetria, curtose e bimodalidade. Algumas delas foram demonstradas como: simetria, função quantílica, algumas expansões, os momentos incompletos ordinários, desvios médios e a função geradora de momentos. A flexibilidade das novas distrições foram comparada com os modelos: skew-normal, beta-normal, Kumarassuamy-normal e gama-normal. A estimativas dos parâmetros dos modelos foram obtidas pelo método da máxima verossimilhança. Nas aplicações foram utilizados modelos de regressão para dados provenientes de delineamentos inteiramente casualizados (DIC) ou delineamentos casualizados em blocos (DBC). Além disso, para os novos modelos, foram realizados estudos de simulação para verificar as propriedades assintóticas das estimativas de parâmetros. Para verificar a presença de valores extremos e a qualidade dos ajustes foram propostos os resíduos quantílicos e a análise de sensibilidade. Portanto, os novos modelos estão fundamentados em propriedades matemáticas, estudos de simulação computacional e com aplicações para dados de delineamentos experimentais. Podem ser utilizados em ensaios inteiramente casualizados ou em blocos casualizados, principalmente, com dados que apresentem evidências de assimetria, curtose e bimodalidade.