英文标题：
《Identification of inferential parameters in the covariate-normalized
  linear conditional logit model》
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作者：
Philip Erickson
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最新提交年份：
2020
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英文摘要：
  The conditional logit model is a standard workhorse approach to estimating customers\' product feature preferences using choice data. Using these models at scale, however, can result in numerical imprecision and optimization failure due to a combination of large-valued covariates and the softmax probability function. Standard machine learning approaches alleviate these concerns by applying a normalization scheme to the matrix of covariates, scaling all values to sit within some interval (such as the unit simplex). While this type of normalization is innocuous when using models for prediction, it has the side effect of perturbing the estimated coefficients, which are necessary for researchers interested in inference. This paper shows that, for two common classes of normalizers, designated scaling and centered scaling, the data-generating non-scaled model parameters can be analytically recovered along with their asymptotic distributions. The paper also shows the numerical performance of the analytical results using an example of a scaling normalizer.
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中文摘要：
条件logit模型是使用选择数据估计客户产品特征偏好的标准方法。然而，大规模使用这些模型可能会由于大值协变量和softmax概率函数的组合而导致数值不精确和优化失败。标准的机器学习方法通过对协变量矩阵应用标准化方案，将所有值缩放到某个区间（例如单位单纯形）来缓解这些问题。虽然在使用模型进行预测时，这种规格化是无害的，但它有干扰估计系数的副作用，这是对推理感兴趣的研究人员所必需的。本文表明，对于两类常见的规范化子，指定标度和中心标度，生成非标度模型参数的数据可以解析地恢复及其渐近分布。本文还以一个标度归一化器为例，展示了分析结果的数值性能。
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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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