Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects

Unique parametrizations of models are very important for parameter interpretation and consistency of estimators. In this paper we analyze the identifiability of a general class of finite mixtures of multinomial logits with varying and fixed effects, which includes the popular multinomial logit and conditional logit models. The application of the general identifiability conditions is demonstrated on several important special cases and relations to previously established results are discussed. The main results are illustrated with a simulation study using artificial data and a marketing dataset of brand choices.     

Non-linear state space models with partially specified distributions on states

The author argues, through the discussion of several examples, why models like the power steady model (Smith 1979, 1981a) are attractive not only because of the simplicity of their one-step-ahead forecast distributions but also because they can be justified through sets of properties that any gradually evolving process may be expected to satisfy.     

线性模拟电路故障诊断的分组诊断法

本文将故障验证技术中的伪电路法和支路诊断法结合起来,提出了线性模拟电路故障诊断的分组诊断法。它将故障网络的电路元件分成“好”的组1,和“坏”的组2,导出支路故障诊断方程和允许分组的拓扑条件。该方法还可应用于非线性两端电阻网络的故障诊断。分组诊断法具有如下特点:诊断方程阶数低;特别是不必穷举各种分组的组合,同时又使允许分组的检验不必通过任何运算而可在电路图上直接进行;在诊断中结合耦合表调整分组,可加快诊断速度,减小计算量。