No description available.

The main aim of the thesis was to describe the succession of several abandoned fields under ploughing and discuss ploughing as a possible management for establishing and maintaining species-rich communities of fallows. Actual vegetation, its development over time and soil seed bank were studied. Experimental small-area ploughing was established at five abandoned fields in the southeastern part of the Podyjí National Park. Three strips with different types of management were founded: A) strip ploughed each year, B) strip ploughed once at the beginning of the experiment, C) control meadow without intervention. Vegetation development was evaluated through a series of relèves recorded during the years 2009-2012. The species composition of the seed bank was described using the seedling-emergence method and subsequent elutriation of soil samples. Similarity indexes, Longevity index (LI) and concept of RCS strategies were used. The vegetation of each-year plowed sites (A) consisted mostly of annual weeds and ruderal species with R-strategy and high LI. Grassland vegetation (C) consisted mostly of grasses and perennial herbs with C-strategy and lower LI. The vegetation of older fallows (B) was at first most similar to one-year fallow (A), but during the 4 years of succession has moved closer to the meadow...

La forte demande de résultats à un niveau géographique fin, notamment à partir d'enquêtes nationales, a mis en évidence la fragilité des estimations sur petits domaines. Cette thèse propose d'y remédier avec des méthodes spécifiques basées sur le plan de sondage. Celles-ci reposent sur la constructionde nouvelles pondérations pour chaque unité statistique. La première méthode consiste à optimiser le redressement du sous-échantillon d'une enquête inclusdans un domaine. La deuxième repose sur la construction de poids dépendant à la fois des unités statistiques et des domaines. Elle consiste à scinder les poids de sondage de l'estimateur global tout en respectant deux contraintes : 1/ la somme des estimations sur toute partition en domaines est égale à l'estimation globale ; 2/ le système de pondération pour un domaine particulier satisfait les propriétés de calage sur les variables auxiliaires connues pour le domaine. L'estimateur par scission ainsi obtenu se comporte de manière quasi analogue au célèbre estimateur blup (meilleur prédicteur linéaire sans biais). La troisième méthode propose une réécriture de l'estimateur blup sous la forme d'un estimateur linéaire homogène, en adoptant une approche basée sur le plan de sondage, bien que l'estimateur dépende d'un modèle. De nouveaux estimateurs blup modifiés sont obtenus. Leur précision, estimée par simulation avec application sur des données réelles, est assez proche de celle de l'estimateur blup standard. Les méthodes développées dans cette thèse sont ensuite appliquées à l'estimation d'indicateurs de la mobilité locale à partir de l'Enquête Nationale sur les Transports et les Déplacements 2007-2008. Lorsque la taille d'un domaine est faible dans l'échantillon, les estimations obtenues avec la première méthode perdent en précision, alors que la précision reste satisfaisante pour les deux autres méthodes. / The strong demand for results at a detailed geographic level, particularly from national surveys, has raised the problem of the fragility of estimates for small areas. This thesis addresses this issue with specific methods based on the sample design. These ones consist of building new weights for each statistical unit. The first method consists of optimizing the re-weighting of a subsample survey included in an area. The second one is based on the construction of weights that depend on the statistical units as well as the areas. It consists of splitting the sampling weights of the overall estimator while satisfying two constraints : 1/ the sum of the estimates on every partition into areas is equal to the overall estimate ; 2/ the system of weights for a given area satisfies calibration properties on known auxiliary variables at the level of the area. The split estimator thus obtained behaves almost similarly as the well-known blup (best linear unbiased predictor) estimator. The third method proposes a rewriting of the blup estimator, although model-based, in the form of a homogenous linear estimator from a design-based approach. New modified blup estimators are obtained. Their precision, estimated by simulation with an application to real data, is quite close to that of the standard blup estimator. Then, the methods developed in this thesis are applied to the estimation of local mobility indicators from the 2007-2008 French National Travel Survey. When the size of an area is small in the sample, the estimates obtained with the first method are not precise enough whereas the precision remains satisfactory for the two other methods.

L'existence des inégalités sociale de santé (ISS) est solidement établie en France. La mortalité infantile et néonatale sont reconnues comme des indicateurs de l'état de santé d'une population. En dépit de nombreux facteurs de risques déjà identifiés, une part des ISS demeurent inexpliquées ; les nuisances environnementales sont suspectées. L'objectif de la thèse est d'analyser par une approche spatio-temporelle la contribution de l'exposition au dioxyde d'azote aux inégalités sociales de mortalité infantile et néonatale en France 2000-2009. L'étude est épidémiologique de type écologique dans les agglomérations de Lille, Paris, Lyon, et Marseille. L'unité géographique est l'IRIS. Les cas recueillis dans les mairies ont été géocodés en utilisant l'adresse de résidence des parents. Les données socioéconomiques estimées à partir des recensements de 1999, 2006 ont été utilisées dans un indice composite définissant la défaveur socioéconomique globale. Les concentrations moyennes de dioxyde d'azote ont été modélisées par les réseaux de surveillance de la qualité de l'air. Des modèles statistiques additifs généralisés ont permis de prendre en compte l'autocorrélation spatiale et de générer des cartes à l'aide de lissage sur la longitude et la latitude tout en ajustant sur les variables d'intérêt. A l'aide d'une approche innovante, les résultats ont démontré l'existence de zones d'inégalités socio-spatiales, environnementale ou le cumul d'inégalités de mortalité infantile et néonatale. Ces résultats sont ville-spécifique, ils varient selon la période d'étude et l'évènement sanitaire étudié démontrant ainsi la difficulté de généraliser ces observations à l'échelle nationale / In France, existence of social health inequalities (SHI) has well established. Infant and neonatal mortality are recognized as indicators of the health status of a population. In spite of numerous risk factors already identified, a part of these inequalities remain unexplained, environmental nuisances are suspected. The thesis objectives were to analyze by a spatial and temporal approach, the contribution of exposure to nitrogen dioxide to social inequalities in infant and neonatal mortality in France between 2000 and 2009. We conducted an ecological type epidemiological study using the French census block as the geographical unit in the metropolitan areas of Lille, Paris, Lyon, and Marseille. All cases collected in the cities hall were geocoded using address of parent's residence. Socioeconomic data estimated from the 1999, 2006 national census were used in a composite index which encompasses multiple dimensions to analyze global deprivation. Average nitrogen dioxide concentrations were modeled by the air quality monitoring networks. Generalized additive models allowed to take into account spatial autocorrelation and generate maps using smoothing on longitude and latitude while adjusting for covariates of interest. Using an innovative approach, results highlight the existence of socio-spatial, environmental or cumulate inequalities in infant and neonatal mortality. These results are city-specific, they vary according to the period and the health event demonstrating the difficulty to generalize these observations at the national level

We implement techniques of small area estimation (SAE) to study consumption, a welfare indicator, which is used to assess poverty in the 2003-2004 Nepal Living Standards Survey (NLSS-II) and the 2001 census. NLSS-II has detailed information of consumption, but it can give estimates only at stratum level or higher. While population variables are available for all households in the census, they do not include the information on consumption; the survey has the `population' variables nonetheless. We combine these two sets of data to provide estimates of poverty indicators (incidence, gap and severity) for small areas (wards, village development committees and districts). Consumption is the aggregate of all food and all non-food items consumed. In the welfare survey the responders are asked to recall all information about consumptions throughout the reference year. Therefore, such data are likely to be noisy, possibly due to response errors or recalling errors. The consumption variable is continuous and positively skewed, so a statistician might use a logarithmic transformation, which can reduce skewness and help meet the normality assumption required for model building. However, it could be problematic since back transformation may produce inaccurate estimates and there are difficulties in interpretations. Without using the logarithmic transformation, we develop hierarchical Bayesian models to link the survey to the census. In our models for consumption, we incorporate the `population' variables as covariates. First, we assume that consumption is noiseless, and it is modeled using three scenarios: the exponential distribution, the gamma distribution and the generalized gamma distribution. Second, we assume that consumption is noisy, and we fit the generalized beta distribution of the second kind (GB2) to consumption. We consider three more scenarios of GB2: a mixture of exponential and gamma distributions, a mixture of two gamma distributions, and a mixture of two generalized gamma distributions. We note that there are difficulties in fitting the models for noisy responses because these models have non-identifiable parameters. For each scenario, after fitting two hierarchical Bayesian models (with and without area effects), we show how to select the most plausible model and we perform a Bayesian data analysis on Nepal's poverty data. We show how to predict the poverty indicators for all wards, village development committees and districts of Nepal (a big data problem) by combining the survey data with the census. This is a computationally intensive problem because Nepal has about four million households with about four thousand households in the survey and there is no record linkage between households in the survey and the census. Finally, we perform empirical studies to assess the quality of our survey-census procedure.

It is customary when presenting a choropleth map of rates or counts to present only the estimates (mean or mode) of the parameters of interest. While this technique illustrates spatial variation, it ignores the variation inherent in the estimates. We describe an approach to present variability in choropleth maps by constructing 100(1-alpha)% simultaneous intervals. The result provides three maps (estimate with two bands). We propose two methods to construct simultaneous intervals from the optimal individual highest posterior density (HPD) intervals to ensure joint simultaneous coverage of 100(1-alpha)%. Both methods exhibit the main feature of multiplying the lower bound and dividing the upper bound of the individual HPD intervals by parameters 0

Model-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data. We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.

Model-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data. We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.

No description available.

No description available.