摘要翻译：
我们提出了一个混合多项式logit(MNL)模型，它利用Dirichlet过程的截短断棒过程表示作为灵活的非参数混合分布。该模型是一个Dirichlet过程混合模型，并与潜在类MNL模型一样，可以容纳异质性的离散表示。然而，与潜在类MNL模型不同的是，由于离散混合分布的复杂性是从证据中推断出来的，所提出的离散选择模型不需要分析师在估计之前确定混合成分的数量。对于离散选择的Dirichlet过程混合模型的后验推理，我们导出了一个期望最大化算法。在仿真研究中，我们证明了所提出的模型框架能够灵活地捕捉不同形状的味觉参数分布。此外，我们以驾驶员路径选择偏好为例对模型框架进行了实证验证，发现所提出的离散选择Dirichlet过程混合模型在样本内拟合和样本外预测能力方面都优于潜在类MNL模型和具有常见参数混合分布的混合MNL模型。与现有的建模方法相比，所提出的离散选择模型大大缩短了规范搜索，因为它依赖于限制性较小的参数假设，并且不要求分析师在估计之前指定离散混合分布的复杂性。
---
英文标题：
《A Dirichlet Process Mixture Model of Discrete Choice》
---
作者：
Rico Krueger, Akshay Vij, Taha H. Rashidi
---
最新提交年份：
2018
---
分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--
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。
--
一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
--
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
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
英文摘要：
  We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a latent class MNL model. Yet, unlike a latent class MNL model, the proposed discrete choice model does not require the analyst to fix the number of mixture components prior to estimation, as the complexity of the discrete mixing distribution is inferred from the evidence. For posterior inference in the proposed Dirichlet process mixture model of discrete choice, we derive an expectation maximisation algorithm. In a simulation study, we demonstrate that the proposed model framework can flexibly capture differently-shaped taste parameter distributions. Furthermore, we empirically validate the model framework in a case study on motorists' route choice preferences and find that the proposed Dirichlet process mixture model of discrete choice outperforms a latent class MNL model and mixed MNL models with common parametric mixing distributions in terms of both in-sample fit and out-of-sample predictive ability. Compared to extant modelling approaches, the proposed discrete choice model substantially abbreviates specification searches, as it relies on less restrictive parametric assumptions and does not require the analyst to specify the complexity of the discrete mixing distribution prior to estimation.
---
PDF链接：
https://arxiv.org/pdf/1801.06296