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Deprivation analysis based on Bayesian latent class models

机译:基于贝叶斯潜在类模型的剥夺分析

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This article seeks to measure deprivation among Portuguese households, taking into account four well-being dimensions - housing, durable goods, economic strain and social relationships - with survey data from the European Community Household Panel. We propose a multi-stage approach to a cross-sectional analysis, side-stepping the sparse nature of the contingency tables caused by the large number of variables considered and bringing together partial and overall analyses of deprivation that are based on Bayesian latent class models via Markov Chain Monte Carlo methods. The outcomes demonstrate that there was a substantial improvement on household overall well-being between 1995 and 2001. The dimensions that most contributed to the risk of household deprivation were found to be economic strain and social relationships.
机译:本文力图通过四个方面的评估来衡量葡萄牙家庭的贫困状况:住房,耐用品,经济压力和社会关系,并使用欧洲共同体家庭小组的调查数据。我们提出了一种多阶段的横截面分析方法,避开了由大量考虑的变量引起的列联表的稀疏性质,并将基于贝叶斯潜在类模型的剥夺的部分和整体分析结合在一起马尔可夫链蒙特卡罗方法。结果表明,1995年至2001年间,家庭总体福祉得到了显着改善。发现造成家庭贫困风险最大的因素是经济压力和社会关系。

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