Doctor of Philosophy / Department of Agricultural Economics / Allen M. Featherstone / This dissertation focuses on measuring the efficiency and productivity for agricultural cooperatives in the United States using the data envelopment analysis (DEA) approach. Economic measures such as cost efficiency, economies of scale, and economies of scope are measured by estimating a cost frontier in a multiproduct framework. Productivity growth is measured using the biennial Malmquist index approach. The cost frontier is the basis for calculating cost efficiency, economies of scale, and economies of scope as the cost frontier estimation in a multiproduct approach describes how cost changes as output changes. The estimates of economies of scale and scope have important implications for agricultural cooperatives because most of the cooperatives sell more than one product. Understanding the impact of changing output levels or mixes on the cost structure is helpful to improve the performance of cooperatives. Further, scope economies estimate the percentage of cost savings through product diversification in a multiproduct firm. The trade-off between cost efficiency and multiproduct scale economies allows the estimation of whether a higher percentage of cost can be eliminated by becoming cost efficient or changing the scale of operations. The economic measures are estimated using a single cost frontier (multi-year frontier) and annual cost frontiers. Multiproduct economies of scale and economies of scope exist indicating that increasing scale and product diversification can reduce cost for agricultural cooperatives. The mean values of product-specific economies of scale for all outputs are close to one indicating that cooperatives are operating close to constant returns to scale. The comparison between cost efficiency and scale economies suggests that smaller cooperatives can save a higher percentage of cost by increasing the scale of operations rather than just becoming cost efficient. Because larger incentives exist for small cooperatives to increase scale, mergers will likely continue until economies of scale are exhausted in the industry. Annual estimates show that agricultural cooperatives have become less cost efficient over time, but economies of scale and economies of scope remain consistent across years. Many agricultural cooperatives face economies of scale indicating that variable returns to scale as opposed to constant returns to scale is the appropriate technology for modeling agricultural farm marketing and supply cooperatives. Further, the Kolmogorov-Smirnov (KS) test and two sample t-test are used to examine whether economic measures estimated from a single frontier and annual frontiers are statistically different. The KS test and t-test indicate that economic measures obtained from the single frontier are statistically different from those measures calculated from annual frontiers. This indicates that the cost frontier has shifted over time. Productivity growth of agricultural cooperatives is estimated using the biennial Malmquist productivity index (BMI) under variable returns to scale over the period 2005 to 2014. The BMI avoids numerical infeasibilities under variable returns to scale compared to traditional methods. The BMI is decomposed into efficiency change and technical change to evaluate the sources of productivity growth. Overall, agricultural cooperatives gained 34% cumulative productivity growth during the decade allocated by -2% and 37% cumulative technical efficiency change and technical change over the study period. Technical change was the major source of productivity growth rather than efficiency change. Cooperatives can achieve higher productivity by increasing managerial efficiency and by investing in technology.

<p>This paper offers to measure efficiency of banks in Sub Saharan Africa and its determining input andout put factors on two fonts. At this purpose, we applied the first font; Data Envelopment Analysis(DEA) for assessing efficiency level. The actual and target level of inputs/outputs to foster efficiencyare shown in the results. Secondly, the banks ratio analysis measuring banks performance throughreturns volatility for each bank, asset utilization and provision for bad and doubtful debts over thestudy period are all used as tools for this analysis. Our results suggest that Sub Saharan AfricanBanks are about 98.35% efficient. We are aware that the level of efficiency could be subject to up anddown swing if environmental factors influencing banks efficiency where taken into consideration.Finally, our result (DEA) is more sensitive to loans, other liabilities, other non interest expense,securities and deposit.</p>

This paper offers to measure efficiency of banks in Sub Saharan Africa and its determining input andout put factors on two fonts. At this purpose, we applied the first font; Data Envelopment Analysis(DEA) for assessing efficiency level. The actual and target level of inputs/outputs to foster efficiencyare shown in the results. Secondly, the banks ratio analysis measuring banks performance throughreturns volatility for each bank, asset utilization and provision for bad and doubtful debts over thestudy period are all used as tools for this analysis. Our results suggest that Sub Saharan AfricanBanks are about 98.35% efficient. We are aware that the level of efficiency could be subject to up anddown swing if environmental factors influencing banks efficiency where taken into consideration.Finally, our result (DEA) is more sensitive to loans, other liabilities, other non interest expense,securities and deposit.

As item response theory (IRT) has developed and is widely applied, investigating the fit of a parametric model becomes an important part of the measurement process when implementing IRT. The usefulness and successes of IRT applications rely heavily on the extent to which the model reflects the data, so it is necessary to evaluate model-data fit by gathering sufficient evidence before any model application. There is a lack of promising solutions on the detection of model misfit in IRT. In addition, commonly used fit statistics are not satisfactory in that they often do not possess desirable statistical properties and lack a means of examining the magnitude of misfit (e.g., via graphical inspections). In this dissertation, a newly-proposed nonparametric approach, RISE was thoroughly and comprehensively studied. Specifically, the purposes of this study are to (a) examine the promising fit procedure, RISE, (b) compare the statistical properties of RISE with that of the commonly used goodness-of-fit procedures, and (c) investigate how RISE may be used to examine the consequences of model misfit. To reach the above-mentioned goals, both a simulation study and empirical study were conducted. In the simulation study, four factors including ability distribution, sample size, test length and model were varied as the factors which may influence the performance of a fit statistic. The results demonstrated that RISE outperformed G2 and S-X2 in that it controlled Type I error rates and provided adequate power under all conditions. In the empirical study, the three fit statistics were applied to one empirical data and the misfitting items were flagged. RISE and S-X2 detected reasonable numbers of misfitting items while G2 detected almost all items when sample size is large. To further demonstrate an advantage of RISE, the residual plot on each misfitting item was shown. Compared to G2 and S-X2, RISE gave a much clearer picture of the location and magnitude of misfit for each misfitting item. Other than statistical properties and graphical displays, the score distribution and test characteristic curve (TCC) were investigated as model misfit consequence. The results indicated that for the given data, there was no practical consequence on classification before and after replacement of misfitting items detected by three fit statistics.

Ce manuscrit propose une approche méthodologique pour la constitution d’une chaîne complète de vidéosurveillance pour des environnements naturellement encombrés. Nous identifions et levons un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à l’acquisition de séquences vidéo en milieu naturel, 2) au traitement d’images, 3) au suivi multi-cibles, 4) à la découverte et la modélisation de motifs comportementaux récurrents, et 5) à la fusion de données. Le contexte applicatif de nos travaux est la surveillance apicole, et en particulier, l’étude des trajectoires des abeilles en vol devant la ruche. De ce fait, cette thèse se présente également comme une étude de faisabilité et de prototypage dans le cadre des deux projets interdisciplinaires EPERAS et RISQAPI (projets menées en collaboration avec l’INRA Magneraud et le Muséum National d’Histoire Naturelle). Il s’agit pour nous informaticiens et pour les biologistes qui nous ont accompagnés, d’un domaine d’investigation totalement nouveau, pour lequel les connaissances métiers, généralement essentielles à ce genre d’applications, restent encore à définir. Contrairement aux approches existantes de suivi d’insectes, nous proposons de nous attaquer au problème dans l’espace à trois dimensions grâce à l’utilisation d’une caméra stéréovision haute fréquence. Dans ce contexte, nous détaillons notre nouvelle méthode de détection de cibles appelée segmentation HIDS. Concernant le calcul des trajectoires, nous explorons plusieurs approches de suivi de cibles, s’appuyant sur plus ou moins d’a priori, susceptibles de supporter les conditions extrêmes de l’application (e.g. cibles nombreuses, de petite taille, présentant un mouvement chaotique). Une fois les trajectoires collectées, nous les organisons selon une structure de données hiérarchique et mettons en œuvre une approche Bayésienne non-paramétrique pour la découverte de comportements émergents au sein de la colonie d’insectes. L’analyse exploratoire des trajectoires issues de la scène encombrée s’effectue par classification non supervisée, simultanément sur des niveaux sémantiques différents, et où le nombre de clusters pour chaque niveau n’est pas défini a priori mais est estimé à partir des données. Cette approche est dans un premier temps validée à l’aide d’une pseudo-vérité terrain générée par un Système Multi-Agents, puis dans un deuxième temps appliquée sur des données réelles. / This manuscript provides the basis for a complete chain of videosurveillence for naturally cluttered environments. In the latter, we identify and solve the wide spectrum of methodological and technological barriers inherent to : 1) the acquisition of video sequences in natural conditions, 2) the image processing problems, 3) the multi-target tracking ambiguities, 4) the discovery and the modeling of recurring behavioral patterns, and 5) the data fusion. The application context of our work is the monitoring of honeybees, and in particular the study of the trajectories bees in flight in front of their hive. In fact, this thesis is part a feasibility and prototyping study carried by the two interdisciplinary projects EPERAS and RISQAPI (projects undertaken in collaboration with INRA institute and the French National Museum of Natural History). It is for us, computer scientists, and for biologists who accompanied us, a completely new area of investigation for which the scientific knowledge, usually essential for such applications, are still in their infancy. Unlike existing approaches for monitoring insects, we propose to tackle the problem in the three-dimensional space through the use of a high frequency stereo camera. In this context, we detail our new target detection method which we called HIDS segmentation. Concerning the computation of trajectories, we explored several tracking approaches, relying on more or less a priori, which are able to deal with the extreme conditions of the application (e.g. many targets, small in size, following chaotic movements). Once the trajectories are collected, we organize them according to a given hierarchical data structure and apply a Bayesian nonparametric approach for discovering emergent behaviors within the colony of insects. The exploratory analysis of the trajectories generated by the crowded scene is performed following an unsupervised classification method simultaneously over different levels of semantic, and where the number of clusters for each level is not defined a priori, but rather estimated from the data only. This approach is has been validated thanks to a ground truth generated by a Multi-Agent System. Then we tested it in the context of real data.