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The dissertation consists of four self-contained essays on consumption. Essays 1 and 2 consider different measures of aggregate consumption, and Essays 3 and 4 consider how the distributions of income and wealth affect consumption from a macro and micro perspective, respectively. Essay 1 considers the empirical practice of seemingly interchangeable use of two measures of consumption; total consumption expenditure and consumption expenditure on nondurable goods and services. Using data from Sweden and the US in an error correction model, it is shown that consumption functions based on the two measures exhibit significant differences in several aspects of econometric modelling. Essay 2, coauthored with Thomas Holgersson, considers derivation of a univariate and a multivariate version of a test for asymmetry, based on the third central moment. The logic behind the test is that the dependent variable should correspond to the specification of the econometric model; symmetric with linear models and asymmetric with non-linear models. The main result in the empirical application of the test is that orthodox theory seems to be supported for consumption of both nondurable and durable consumption. The consumption of durables shows little deviation from symmetry in the four-country sample, while the consumption of nondurables is shown to be asymmetric in two out of four cases, the UK and the US. Essay 3 departs from the observation that introducing income uncertainty makes the consumption function concave, implying that the distributions of wealth and income are omitted variables in aggregate Euler equations. This implication is tested through estimation of the distributions over time and augmentation of consumption functions, using Swedish data for 1963-2000. The results show that only the dispersion of wealth is significant, the explanation of which is found in the marked changes of the group of households with negative wealth; a group that according to a concave consumption function has the highest marginal propensity to consume. Essay 4 attempts to empirically specify the nature of the alleged concavity of the consumption function. Using grouped household level Swedish data for 1999-2001, it is shown that the marginal propensity to consume out of current resources, i.e. current income and net wealth, is strictly decreasing in current resources and net wealth, but approximately constant in income. Also, an empirical reciprocal to the stylized theoretical consumption function is estimated, and shown to bear a close resemblance to the theoretical version.

Les données d’expression génique sont reconnues comme étant de grande dimension, etnécessitant l’emploi de méthodes statistiques adaptées. Mais dans le contexte des essaisvaccinaux, d’autres mesures, comme par exemple les mesures de cytométrie en flux, sontégalement de grande dimension. De plus, ces données sont souvent mesurées de manièrelongitudinale. Ce travail est bâti sur l’idée que l’utilisation d’un maximum d’informationdisponible, en modélisant les connaissances a priori ainsi qu’en intégrant l’ensembledes différentes données disponibles, améliore l’inférence et l’interprétabilité des résultatsd’analyses statistiques en grande dimension. Tout d’abord, nous présentons une méthoded’analyse par groupe de gènes pour des données d’expression génique longitudinales. Ensuite,nous décrivons deux analyses intégratives dans deux études vaccinales. La premièremet en évidence une sous-expression des voies biologiques d’inflammation chez les patientsayant un rebond viral moins élevé à la suite d’un vaccin thérapeutique contre le VIH. Ladeuxième étude identifie un groupe de gènes lié au métabolisme lipidique dont l’impactsur la réponse à un vaccin contre la grippe semble régulé par la testostérone, et donc liéau sexe. Enfin, nous introduisons un nouveau modèle de mélange de distributions skew t àprocessus de Dirichlet pour l’identification de populations cellulaires à partir de donnéesde cytométrie en flux disponible notamment dans les essais vaccinaux. En outre, nousproposons une stratégie d’approximation séquentielle de la partition a posteriori dans lecas de mesures répétées. Ainsi, la reconnaissance automatique des populations cellulairespourrait permettre à la fois une avancée pratique pour le quotidien des immunologistesainsi qu’une interprétation plus précise des résultats d’expression génique après la priseen compte de l’ensemble des populations cellulaires. / Gene expression data is recognized as high-dimensional data that needs specific statisticaltools for its analysis. But in the context of vaccine trials, other measures, such asflow-cytometry measurements are also high-dimensional. In addition, such measurementsare often repeated over time. This work is built on the idea that using the maximum ofavailable information, by modeling prior knowledge and integrating all data at hand, willimprove the inference and the interpretation of biological results from high-dimensionaldata. First, we present an original methodological development, Time-course Gene SetAnalysis (TcGSA), for the analysis of longitudinal gene expression data, taking into accountprior biological knowledge in the form of predefined gene sets. Second, we describetwo integrative analyses of two different vaccine studies. The first study reveals lowerexpression of inflammatory pathways consistently associated with lower viral rebound followinga HIV therapeutic vaccine. The second study highlights the role of a testosteronemediated group of genes linked to lipid metabolism in sex differences in immunologicalresponse to a flu vaccine. Finally, we introduce a new model-based clustering approach forthe automated treatment of cell populations from flow-cytometry data, namely a Dirichletprocess mixture of skew t-distributions, with a sequential posterior approximation strategyfor dealing with repeated measurements. Hence, the automatic recognition of thecell populations could allow a practical improvement of the daily work of immunologistsas well as a better interpretation of gene expression data after taking into account thefrequency of all cell populations.

In this paper, we first present the basic principles of set theory and combinatorial analysis which are the most useful tools in computing probabilities. Then, we show some important properties derived from axioms of probability. Conditional probabilities come into play not only when some partial information is available, but also as a tool to compute probabilities more easily, even when partial information is unavailable. Then, the concept of random variable and its some related properties are introduced. For univariate random variables, we introduce the basic properties of some common discrete and continuous distributions. The important properties of jointly distributed random variables are also considered. Some inequalities, the law of large numbers and the central limit theorem are discussed. Finally, we introduce additional topics the Poisson process.