Evaluation of the Aeroplan distinction status program desirability

Aeroplan Difference Status计划由我们的合作伙伴Aimia Canada Inc.于2014年推出，旨在**进一步奖励和表彰该计划最积极和最活跃的成员。在该计划下，**会员有资格根据上一历年的合格里程积累获得三个等级中的一个。**每个等级都提供奖励和晋升机会等福利。我们的合作伙伴想要确定身份**计划是否对会员的收入行为有影响，如果有，针对哪种类型的会员。**这个项目的困难在于检测具有自然变异性的时间序列中的具体变化，该时间序列可能包括**非平稳分量、趋势和季节性分量。检测受状态影响的收益率(ER)局部变化**可以看作是一个分两步进行的过程。首先，必须检测ER时间序列中的局部可变性**变化。其次，ON必须表明这种变化的时间与与状态限定阈值相关的关键时间重合(即接近)**。为了确定这种巧合不是偶然的，应该在几个关键时刻反复观察它们。要获得具有统计学意义的**结果，可能需要将具有相似ER行为的几个成员分组。该项目有两个**目标：**1)通过当地统计指标检测ER局部变异性的局部变化，并将其与**资格门槛时间相关联。2)通过偏离最佳线性**预测来检测ER局部变异性的局部变化，并将其与合格阈值时间相关联。**我们将通过搜索本地统计指标(有待**开发)与与状态合格阈值相关的关键时间之间的重合来实现目标1)。我们将通过**首先开发ER序列的线性预测器，然后通过检查每个ER序列的预测偏差(预测**减去实际值)来实现目标2)。这种偏差应该揭示内质网变异性的变化，这可能**指示行为的变化。

The Aeroplan Distinction Status program was launched in 2014 by our partner Aimia Canada Inc. to give**further rewards and recognition to the program's most engaged and active members. Under that program,**members qualify for one of three tiers based on eligible mileage accumulation from the previous calendar year.**Each tier offers benefits such as rewards and access to promotions. Our partner wants to determine if the status**program has an influence on members earn behaviour, and if so, for what type of members. The difficulty in**this project is to detect specific changes in a time series that has a natural variability that may include**non-stationary components, trends and seasonal components. The detection of Earning Rate (ER) local changes**that are influenced by status can be regarded as a two-step procedure. First, one must detect local variability**changes in the ER time series. Second, on must show that the times of such changes coincide (i.e. are close to)**with the critical times associated with status qualification thresholds. To establish that such coincidences are**not accidental, they should be observed repeatedly for several critical times. Obtaining statistically significant**results may require the grouping of several members having similar ER behaviours. The project has two**objectives:**1) Detect local changes in the ER local variability via local statistical indicators and correlate them with**qualification threshold times. 2) Detect local changes in the ER local variability via deviations from best linear**prediction and correlate them with qualification threshold times.**We will achieve objective 1) by searching for coincidences between local statistical measures (to be**developed) and critical times associated with status qualification thresholds. We will achieve objective 2) by**first developing a linear predictor of the ER series, and then by examining the prediction deviation (prediction**minus actual value) for each ER series. This deviation should reveal changes in the ER variability that could be**indicative of behaviour change.