摘要翻译：
条件分布函数是分析计量经济学和统计学中一类广泛问题的重要统计对象。我们提出了条件分布函数的柔性高斯表示，并给出了其全局估计的凹似然公式。我们得到了满足条件分布函数单调性的解，包括在一般的非规范条件下和有限样本条件下。给出了相应的最大似然估计的一个Lasso型惩罚版本，将估计分析的范围扩展到稀疏模型。给出了条件分布、分位点和密度函数的推论和估计结果，并用一个经验例子和模拟加以说明。
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英文标题：
《Gaussian Transforms Modeling and the Estimation of Distributional
Regression Functions》
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作者：
Richard Spady, Sami Stouli
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最新提交年份：
2020
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分类信息：

一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
Conditional distribution functions are important statistical objects for the analysis of a wide class of problems in econometrics and statistics. We propose flexible Gaussian representations for conditional distribution functions and give a concave likelihood formulation for their global estimation. We obtain solutions that satisfy the monotonicity property of conditional distribution functions, including under general misspecification and in finite samples. A Lasso-type penalized version of the corresponding maximum likelihood estimator is given that expands the scope of our estimation analysis to models with sparsity. Inference and estimation results for conditional distribution, quantile and density functions implied by our representations are provided and illustrated with an empirical example and simulations.
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