首页> 外文期刊>The Annals of applied statistics >BAYESIAN STRUCTURED ADDITIVE DISTRIBUTIONAL REGRESSION WITH AN APPLICATION TO REGIONAL INCOME INEQUALITY IN GERMANY
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BAYESIAN STRUCTURED ADDITIVE DISTRIBUTIONAL REGRESSION WITH AN APPLICATION TO REGIONAL INCOME INEQUALITY IN GERMANY

机译:贝叶斯结构化分配分布回归及其在德国区域收入不平等中的应用

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We propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, interaction surfaces or other (possibly nonstandard) basis function representations. To enforce specific properties of the functional effects such as smoothness, informative multivariate Gaussian priors are assigned to the basis function coefficients. Inference can then be based on computationally efficient Markov chain Monte Carlo simulation techniques where a generic proceduremakes use of distribution-specific iteratively weighted least squares approximations to the full conditionals. The framework of distributional regression encompasses many special cases relevant for treating nonstandard response structures such as highly skewed nonnegative responses, overdispersed and zero-inflated counts or shares including the possibility for zero-and one-inflation. We discuss distributional regression along a study on determinants of labour incomes for full-time working males in Germany with a particular focus on regional differences after the German reunification. Controlling for age, education, work experience and local disparities, we estimate full conditional income distributions allowing us to study various distributional quantities such as moments, quantiles or inequality measures in a consistent manner in one joint model. Detailed guidance on practical aspects of model choice including the selection of several competing distributions for labour incomes and the consideration of different covariate effects on the income distribution complete the distributional regression analysis. We find that next to a lower expected income, full-time working men in East Germany also face a more unequal income distribution than men in the West, ceteris paribus.
机译:我们为分布回归模型中的推理提出了一个通用的贝叶斯框架,其中潜在复杂响应分布的每个参数(不仅是均值)都与结构化加性预测变量相关。后者是由各种不同的功能效果类型(例如非线性效果,空间效果,随机系数,交互作用表面或其他(可能是非标准的)基本功能表示形式)组成的。为了增强功能效果的特定属性(例如平滑度),将信息多变量高斯先验值分配给基本函数系数。然后可以基于计算效率高的Markov链蒙特卡罗模拟技术进行推论,其中通用过程利用了针对特定条件的特定于分布的迭代加权最小二乘近似。分布回归的框架包含许多与处理非标准响应结构相关的特殊情况,例如高度偏斜的非负响应,过度分散的和零膨胀的计数或份额,包括零和一膨胀的可能性。我们在研究德国全职工作男性的劳动收入决定因素的基础上讨论了分布回归,特别关注了德国统一后的地区差异。通过控制年龄,教育程度,工作经验和局部差异,我们估算了有条件的全部收入分配,从而使我们能够在一个联合模型中以一致的方式研究各种分配量,例如矩,分位数或不平等度量。有关模型选择的实际方面的详细指导,包括为劳动收入选择几种竞争性分配,以及考虑不同的协变量对收入分配的影响,完成了分布回归分析。我们发现,除了较低的预期收入外,东德的全日制工作人员也比西方的ceteris paribus面临更不平等的收入分配。

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