摘要翻译：
本文提出了一个具有灵活类隶属度的半非参数潜在类选择模型(LCCM)。该模型采用混合模型作为对传统随机效用规范的替代方法来制定潜在类，目的是在预测精度和选择过程中异质性的表示等方面对两种方法进行比较。混合模型是一种基于参数模型的聚类技术，在机器学习、数据挖掘和模式识别等领域得到了广泛的应用。提出了一种基于期望最大化(EM)的模型估计算法。通过两个不同的出行方式选择行为案例研究，在参数估计符号、时间值、统计拟合优度和交叉验证检验的基础上，将该模型与传统的离散选择模型进行了比较。结果表明，混合模型在不削弱选择模型的行为和经济可解释性的情况下，提供了更好的样本外预测精度，以及更好的异质性表征，从而提高了潜在类选择模型的整体性能。
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英文标题：
《Semi-nonparametric Latent Class Choice Model with a Flexible Class
  Membership Component: A Mixture Model Approach》
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作者：
Georges Sfeir, Maya Abou-Zeid, Filipe Rodrigues, Francisco Camara
  Pereira, Isam Kaysi
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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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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：
  This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process. Mixture models are parametric model-based clustering techniques that have been widely used in areas such as machine learning, data mining and patter recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates' signs, value of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample prediction accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.
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PDF链接：
https://arxiv.org/pdf/2007.02739