This thesis deals with inductive flowmeters, specifically insertion inductive flowmeters. It examines in detail a blueprint of such flowmeter, consisting of probe, electromagnet, electrodes, exciting module and evaluation of signal. Gauging of a flowmeter designed this way, measuring data, charts, evaluation and interpretation. Consequent measurement in a calibration laboratory to elaborate, evaluate and interpret the results.

MSc (Statistics) / Department of Statistics / Over the years researchers have been modeling inflation rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate, does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear models and to establish the existence of nonlinear patterns in the monthly rates of inflation between the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level, and hence we can conclude that the rate of inflation was stationary over the period under consideration. The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable characteristic feature in most time series. Monthly rates of inflation were modeled using threshold models and their fitness and forecasting performance were compared with Autoregressive (AR ) models. Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess each of the fitted models such that the model with the minimum value of AIC and BIC, was judged the best model. Additionally, the fitted models were compared according to their forecasting performance using a criterion called mean absolute percentage error (MAPE). The model with the minimum MAPE emerged as the best forecast model and then the model was used to forecast monthly inflation rates for the year 2017. The rationale for choosing this type of model is contingent on the behaviour of the time-series data. Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform better than linear models. The study found that the SETAR and LSTAR models fit the data best. The simple AR models however, out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains i made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which can use the data to put in place coherent monetary and fiscal policies that would put the anticipated increase in inflation under control.

We aim at generalizing the celebrated portfolio optimization problem "à la Merton", where the asset evolution is steered by a self-exciting jump-diffusion process. We first define the rigorous mathematical framework needed to introduce the stochastic optimal control problem we are interesting in. Then, we provide a proof for a specific version of the Dynamic Programming Principle (DPP) with respect to the general class of self-exciting processes under study. After, we state the Hamilton-Jacobi-Bellman (HJB) equation, whose solution gives the value function for the corresponding optimal control problem. The resulting HJB equation takes the form of a Partial-Integro Differential Equation (PIDE), for which we prove both existence and uniqueness for the solution in the viscosity sense. We further derive a suitable numerical scheme to solve the HJB equation corresponding to the portfolio optimizationproblem. To this end, we also provide a detailed study of solution dependence on the parameters of the problem. The analysis is performed by calibrating the model on ENI asset levels during the COVID-19 worldwide breakout. In particular, the calibration routine is based on a sophisticated Sequential Monte Carlo algorithm.

Les travaux présentés dans cette thèse concernent l'analyse statistique de cas particuliers du processus de Cox. Dans une première partie, nous proposons une synthèse des résultats existants sur le processus power-law (processus d'intensité puissance), synthèse qui ne peut être exhaustive étant donné la popularité de ce processus. Nous considérons une approche bayésienne pour l'inférence des paramètres de ce processus qui nous conduit à introduire et à étudier en détails une distribution que nous appelons loi H-B. Cette loi est une loi conjuguée. Nous proposons des stratégies d'élicitation des hyperparamètres et étudions le comportement des estimateurs de Bayes par des simulations. Dans un deuxième temps, nous étendons ces travaux au cas du processus d’intensité exponentielle (exponential-law process). De la même façon, nous définissons et étudions une loi conjuguée pour l'analyse bayésienne de ce dernier. Dans la dernière partie de la thèse, nous considérons un processus auto-excité qui intègre une covariable. Ce travail est motivé, à l'origine, par un problème de fiabilité qui concerne des données de défaillances de matériels exposés à des environnements sévères. Les résultats sont illustrés par des applications sur des données d'activités orageuses collectées dans deux départements français. Enfin, nous donnons quelques directions de travail et perspectives de futurs développements de l'ensemble de nos travaux. / The work presented in this PhD dissertation concerns the statistical analysis of some particular cases of the Cox process. In a first part, we study the power-law process (PLP). Since the literature for the PLP is abundant, we suggest a state-of-art for the process. We consider the classical approach and recall some important properties of the maximum likelihood estimators. Then we investigate a Bayesian approach with noninformative priors and conjugate priors considering different parametrizations and scenarios of prior guesses. That leads us to define a family of distributions that we name H-B distribution as the natural conjugate priors for the PLP. Bayesian analysis with the conjugate priors are conducted via a simulation study and an application on real data. In a second part, we study the exponential-law process (ELP). We review the maximum likelihood techniques. For Bayesian analysis of the ELP, we define conjugate priors: the modified- Gumbel distribution and Gamma-modified-Gumbel distribution. We conduct a simulation study to compare maximum likelihood estimates and Bayesian estimates. In the third part, we investigate self-exciting point processes and we integrate a power-law covariate model to this intensity of this process. A maximum likelihood procedure for the model is proposed and the Bayesian approach is suggested. Lastly, we present an application on thunderstorm data collected in two French regions. We consider a strategy to define a thunderstorm as a temporal process associated with the charges in a particular location. Some selected thunderstorms are analyzed. We propose a reduced maximum likelihood procedure to estimate the parameters of the Hawkes process. Then we fit some thunderstorms to the power-law covariate self-exciting point process taking into account the associated charges. In conclusion, we give some perspectives for further work.

[pt] A resposta dinâmica de sistemas estruturais não lineares tem sido um item de grande interesse nas pesquisas em engenharia civil. Problemas onde há interação base flexível-estrutura são de grande importância na análise estrutural, já que a maioria das estruturas civis é apoiada sobre sistemas flexíveis (solo ou sistemas de apoio com dissipação de energia). Nesta área, o estudo de sistemas submetidos a ações sísmicas é um tópico relevante, já que estas solicitações têm um grande conteúdo de frequências, o que pode influenciar consideravelmente as respostas da estrutura. Neste contexto, o conhecimento da resposta em frequência de estruturas não lineares sob uma excitação de base é uma ferramenta útil para avaliar os potenciais efeitos de ações sísmicas sobre estes sistemas. Na presente tese é desenvolvida uma metodologia de análise não linear dinâmica de sistemas estruturais reticulados sob excitações de base, considerando não linearidade geométrica e apoios flexíveis, representados por molas unidimensionais, com comportamento elasto-plástico. Através de uma análise paramétrica é avaliada a variabilidade das respostas de sistemas esbeltos submetidos a ações sísmicas reais, sismos artificiais, assim como ações sísmicas sucessivas. O problema no espaço é resolvido pelo método dos elementos finitos. Para a análise em frequência, é apresentada uma metodologia baseada no método do balanço harmônico e no método de Galerkin, juntamente com técnicas de continuação para a obtenção das curvas de ressonância não lineares. O problema no tempo é abordado através da integração das equações de movimento pelos métodos de Runge-Kutta e Newmark, associado ao método de Newton-Raphson. / [en] The dynamic response of nonlinear structures has been a topic of interest in civil engineering research. Problems in which base-structure interaction is present have a great importance in structural analysis, since most structures rests on flexibel systems (soil or supports with dissipation). In this research area, the study of structures under the action of seismic loads represent a relevant topic, since this kind of excitations may excite several vibration modes and thus influence strongly the dynamic response. In this context, the prediction of the nonlinear structural behavior in frequency domain of structures under base excitation is a useful resource to assess the potential effects of sismic loads on these systems. In this thesis, a methodology for nonlinear dynamic analysis of plane frame structures under base excitation is presented considering geometric nonlinearity and elastic supports represented by elasto-plastic unidimensional springs. Trough a parametric analysis, the variability of the dynamic responses of slender structural systems under the actions of real earthquakes, synthetics earthquakes, as well as the action of multiple earthquakes is assessed. The structural systems here analyzed are discretized in space using a nonlinear finite element formulation. For the response in frequency domain, a scheme based on the Balance Harmonic Method and the Galerkin method, in conjunction with continuation methods, is formulated to obtain the nonlinear resonance curves. The nonlinear dynamic response in the time domain is calculated by direct integration of the equations of motion. For this, the Runge-Kutta method and the Newmark method in association with the iterative Newton-Raphson scheme are employed.