The implications on willingness to pay of respondents ignoring specific attributes

Individuals processing the information in a stated choice experiment are typically assumed to evaluate each and every attribute offered within and between alternatives, and to choose their most preferred alternative. However, it has always been thought that some attributes are ignored in this process for many reasons, including a coping strategy to handle ones perception of the complexity of the choice task. Nonetheless, analysts typically proceed to estimate discrete choice models as if all attributes have influenced the outcome to some degree. The cognitive processes used to evaluate trade-offs are complex with boundaries often placed on the task to assist the respondent. These boundaries can include prioritising attributes and ignoring specific attributes. In this paper we investigate the implications of bounding the information processing task by attribute elimination through ignoring one or more attributes. Using a sample of car commuters in Sydney we estimate mixed logit models that assume all attributes are candidate contributors, and models that assume certain attributes are ignored, the latter based on supplementary information provided by respondents. We compare the value of travel time savings under the alternative attribute processing regimes. Assuming that all attributes are not ignored and duly processed, leads to estimates of parameters which produce significantly different willingness to pay (WTP) to that obtained when the exclusion rule is invoked.     

基于门限关联度的传动箱性能检测方法研究

使用振动加速度传感器获取传动箱振动信号，应用概率统计方法建立了传动箱标准正常状态模式，并计算振动信号与标准正常模式的门限关联度．结果表明，所得门限关联度可以反映传动箱当前的性能状况，全部检测过程均在计算机上进行．     

基于相关向量机的电气化铁路用电量预测方法

为了提高用电量预测的精度,提出基于相关向量机回归的预测模型,在时间序列数据的基础上通过拟合训练得出其内在关系,进而可以计算得到较为准确的预测结果。相关向量机具有算法简洁和预测精度高等特点,易于编程使用。为了验证模型的有效性,本文选取2006年3月至2013年2月之间的电气化铁路用电量数据进行模型拟合训练,并预测分析2013年3月至7月的用电量情况。最后,通过对比分析表明相关向量机模型的预测结果比传统回归方法的预测结果更优。