Comparaisons multidimensionnelles de bien-être et de pauvreté ...

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Bibliographie[1] d’Ambrosio C., J. Deutsch et J. Silber (2004), “Multidimensional Approaches to PovertyMeasurement : An Empirical Analysis of Poverty in Belgium, France, Germany,Italy and Spain Based on the European Panel”, 28 th General Conference of The internationalAssociation for Research in Income and Wealth.[2] Anderson, G. (1996), “Nonparametric Tests of Stochastic Dominance in Income Distribution”,Econometrica, 64, 1183-1193.[3] Anderson, G. (2005), “Life Expectancy and Economic Welfare : The Example of africain the 1990s”, Review of Income and Wealth, 51, 455-468.[4] Anderson, G., Crawford, I. et A. Liecester (2005), “Statistical Tests for MultidimensionalPoverty Analysis”, International Conference : The many Dimensions of Poverty,Brasilia, Brazil, August.[5] Asselin, L.-M. (2002), “Multidimensional Poverty : theory”, Institut de MathématiqueGauss.[6] Atkinson, A. B. (2003), “Multidimensional Deprivation : Constrasting Social Welfareand Counting Approaches”, Journal of Economic Inequality, 1, 51-65.[7] Aura, S. (2000), “Statistical Inference for Lorenz Curves Using Simulated Critical Values”,Journal of Income Distribution, 9, 199-213.[8] Barrett, G. F., Bhattacharya, D. et S. G. Donald (2004), “A Comparaison of ConsistentNonparametric Tests for Lorenz Dominance”, Economics Department Seminars, MacquarieUniversity.
Bibliographie 35[9] Barrett, G. F. et S. G. Donald (2003), “Consistent Tests for Stochastic Dominance”,Econometrica, 71, 71-104.[10] Beach, C. M. et R. Davidson (1983), “Distribution-Free Statistical Inference with LorenzCurves and Income Shares”, The Review of Economic Studies, 50, 723-735.[11] Beach, C. M. et J. Richmond (1985), “Joint Confidence Intervals for Income Shares andLorenz Curves”, International Economic Review, 26, 439-450.[12] Berger, R. L. (1982), “Multiparameter Hypothesis Testing and Acceptance Sampling”,Technometrics, 24, 295-300.[13] Berger, R. L. (1989), “Uniformly More Powerful Tests for Hypotheses Concerning LinearInequalities and Normal Means”, Journal of the American Statistical Association,84, 192-199.[14] Berger, R. L. et K. G. Saikali (2002), “More Powerful Tests for the Sign Testing Problem”,Journal of Statistical Planning and Inference, 107, 187-205.[15] Berger, R. L. et D. F. Sinclair (1984), “Testing Hypotheses Concerning Unions of LinearSubspaces”, Journal of the American Statistical Association, 79, 158-163.[16] Betti, G., Cheli, B., Lemmi, A. et N. Pannuzi (2005), “Estimating the Dynamics ofPoverty in Europe Via a TFR Approach : The Experience of the Second Half of theNineties”, 1 rst Meeting of the Society for the Study of Economic Inequality, Palma deMallorca, Spain, July.[17] Bibi S. (2005), “Measuring Poverty in a Multidimensional Perspective : A Review ofLiterature”, PMMA Working Paper 2005-7.[18] Bishop, A. J., Chakraborti, S. et P. D. Thistle (1989), “Asymptotically Distribution-FreeStatistical Inference for Generalized Lorenz Curves”, The Review of Economics andStatistics, 71, 725-727.[19] Bishop, A. J., Formby, J. P. et P. D. Thistle (1989), “Statistical Inference, Income Distributions,and Social Welfare”, In D. J. Slottje (Ed.), Research on Income Inequality, 1,49-91.
Page 1 and 2: YÉLÉ MAWEKI BATANAComparaisons mu
Page 3 and 4: RésuméiiiUn troisième volet cons
Page 5 and 6: Abstractvand poverty, by using a st
Page 7 and 8: Je dédie cette thèse à la mémoi
Page 9 and 10: Table des matièresix3.1 Introducti
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Page 13 and 14: Liste des tableauxxiii5.4 Tests de
Page 15 and 16: Chapitre 1IntroductionCette thèse
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Bibliographie[1] Anderson, G. (1996
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Chapitre 7ConclusionComparer le bie
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