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2022年4月13日 · 在做数据扩张的时候, MNL选择模型的设定要比CL要简单一些的, 因为涉及两个不同的备选项的系数 \bm {\beta}_k 和 \bm {\beta}_l 是彼此"分离的", 所以每一个 k , 我们都可以构造成一系列独立的二项分布. 记 \bm {\beta}= (\bm {\beta}_1', \dots, \bm {\beta}_K')' [3], 其中 \bm {\beta}_k= (\beta_0^ { (k)},\dots,\beta_p^ { (k)})' , 似然函数为.

2023年2月28日 · 当涉及到联合分析时，mnl被用来评估消费者如何权衡不同属性水平之间的组合来做出决策。通过这种方式，企业能够理解哪些产品特性最为重要以及它们之间可能存在怎样的交互作用[^2]。

The MNL Model: the Multi-nomial Logit (MNL) model is a prob-ability distribution over permutations. For a set of nitems, this model is specified by a set of weight parameters w 1;:::;w n. Given a set of items, C, the probability of choosing an item i2Cis P C(i;w) = P w i j2C w j: (1) and the probability of a permutation ˙has the form P(˙;w ...

2024年5月1日 · Two adapted approaches for parameters estimation in MNL models with endogenous variables. New estimators evidence good consistency properties for parameters. The adapted approaches correct the biased estimate of the modal constants. Better estimation of marginal effects and elasticities, both own and cross.

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上一篇《最大似然估计（上）——离散选择模型之十》中讲了最大似然估计（MLE）的基本思想，本文说一说如何利用最大似然估计来确定二项Logit模型的参数。 在二项Logit模型中，决策者 n 只有 i 、 j 两个方案可以选择。 n 选择方案 i 的概率为： P_n (i)= \frac { e^ {β^ {'} X_ {in} } } { e^ {β^ {'} X_ {in} } + e^ {β^ {'} X_ {jn} } } (1) 为方便模型的推导，假设我们只观测到了2个样本：第一个决策者 n（n=1）选择了方案 i，第二个决策者 n（n=2）选择了方案 j 。

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