The general focus of this thesis is on how the family, work and the welfare system are intertwined. A major determinant is the way responsibilities are shared by the state, the market and civil society in different welfare state regimes. An introductory chapter will therefore be dedicated to the development of the social dimension in the process of European integration. A first chapter will then go deeper into the comparative analysis of welfare state regimes, to comment on the provision of welfare in societies with a different mix of state, market and societal welfare roles and to assess the adequacy of existing typologies as reflections of today’s changed socio-economic, political and gender reality. Although they stand strong on their own, these first two chapters also contribute to contextualising the research subject of the remainder of the thesis: the study and comparison of the differential situation of women and men and of mothers and non-mothers on the labour markets of the EU-15 countries as well as of the role of public policies with respect to the employment penalties faced by women, particularly in the presence of young children. In our analysis, employment penalties are understood in three ways: (i) the difference in full-time equivalent employment rates between mothers and non-mothers, (ii) the wage penalty associated with motherhood, and (iii) the wage gap between part-time and full-time workers, considering men and women separately. Besides from a gender point of view, employment outcomes and public policies are thus assessed comparatively for mothers and non-mothers. Because women choose to take part in paid employment, fertility rates will depend on their possibilities to combine employment and motherhood. As a result, motherhood-induced employment penalties and the role of public policies to tackle them should be given priority attention, not just by scholars, but also by politicians and policy-makers. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The objective of the study was to identify the factors that affect the survival lifetime of HIV+ terminal patients in rural district hospitals of Albert Luthuli municipality in the Mpumalanga province of South Africa. A cohort of HIV+ terminal patients was retrospectively followed from 2010 to 2017 until a patient died, was lost to follow-up or was still alive at the end of the observation period. Nonparametric survival analysis and semiparametric survival analysis methods were used to analyse the data. Through Cox proportional hazards regression modelling, it was found that ART adherence (poor, fair, good), Age, Follow-up mass, Baseline sodium, Baseline viral load, Follow CD4 count by Treatment (Regimen 1) interaction and Follow-up lymphocyte by TB history (yes, no) interaction had significant effects on survival lifetime of HIV+ terminal patients (p-values<0.1). Furthermore, through quantile regression modelling, it was found that short, medium and long survival times of HIV+ patients, respectively represented by the 0.1, 0.5 and 0.9 quantiles, were not necessarily significantly affected by the same factors. / Statistics / M. Sc. (Statistics)

In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär). / In many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).

Le but de cette thèse est de développer des outils statistiques permettant d'initialiser et de calibrer les modèles de microsimulation dynamique stochastique, en partant de l’exemple du modèle SimVillages (développé dans le cadre du projet Européen PRIMA). Ce modèle couple des dynamiques démographiques et économiques appliquées à une population de municipalités rurales. Chaque individu de la population, représenté explicitement dans un ménage au sein d’une commune, travaille éventuellement dans une autre, et possède sa propre trajectoire de vie. Ainsi, le modèle inclut-il des dynamiques de choix de vie, d’étude, de carrière, d’union, de naissance, de divorce, de migration et de décès. Nous avons développé, implémenté et testé les modèles et méthodes suivants : 1 / un modèle permettant de générer une population synthétique à partir de données agrégées, où chaque individu est membre d’un ménage, vit dans une commune et possède un statut au regard de l’emploi. Cette population synthétique est l’état initial du modèle. 2 / un modèle permettant de simuler une table d’origine-destination des déplacements domicile-travail à partir de données agrégées. 3 / un modèle permettant d’estimer le nombre d’emplois dans les services de proximité dans une commune donnée en fonction de son nombre d’habitants et de son voisinage en termes de service. 4 / une méthode de calibration des paramètres inconnus du modèle SimVillages de manière à satisfaire un ensemble de critères d'erreurs définis sur des sources de données hétérogènes. Cette méthode est fondée sur un nouvel algorithme d’échantillonnage séquentiel de type Approximate Bayesian Computation. / The purpose of this thesis is to develop statistical tools to initialize and to calibrate dynamic stochastic microsimulation models, starting from their application to the SimVillages model (developed within the European PRIMA project). This model includes demographic and economic dynamics applied to the population of a set of rural municipalities. Each individual, represented explicitly in a household living in a municipality, possibly working in another, has its own life trajectory. Thus, model includes rules for the choice of study, career, marriage, birth children, divorce, migration, and death. We developed, implemented and tested the following models : • a model to generate a synthetic population from aggregate data, where each individual lives in a household in a municipality and has a status with regard to employment. The synthetic population is the initial state of the model. • a model to simulate a table of origin-destination commuting from aggregate data in order to assign a place of work for each individual working outside his municipality of residence. • a sub-model to estimate the number of jobs in local services in a given municipality in terms of its number of inhabitants and its neighbors in terms of service. • a method to calibrate the unknown SimVillages model parameters in order to satisfy a set of criteria. This method is based on a new Approximate Bayesian Computation algorithm using importance sampling. When applied to a toy example and to the SimVillages model, our algorithm is 2 to 8 times faster than the three main sequential ABC algorithms currently available.