Two topics in nonlinear system analysis through fixed point theorems
Two topics in nonlinear system analysis through fixed point theorems
复制标题
通过不动点定理进行非线性系统分析的两个主题
DOI：
10.1145/2487575.2487590
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发表时间：
1994
期刊：
影响因子：
--
通讯作者：
S. Oishi
中科院分区：
文献类型：
--
作者：
S. Oishi
非契约营销领域的一个重要问题是发现顾客生命周期，并评估顾客特征变量对顾客生命周期的影响。不幸的是，传统的分层贝叶斯模型不能识别客户的特征变量对每个客户的影响。为了克服这个问题，我们提出了一个新的生存模型，使用非参数贝叶斯范式与MCMC。传统模型的假设，对数的购买率和辍学率与线性回归，扩展到包括我们的假设的Dirichlet过程混合回归。扩展假设，每个客户属于概率不同的混合回归，从而使我们能够估计不同的影响客户特征变量为每个客户。我们的模型创建了多个客户群体来反映目标数据集的结构。我们的提议的有效性通过涉及真实的电子商务交易数据集和人工数据集的比较得到了证实;它通常可以实现更高的预测性能。此外，我们表明，预选客户群的实际数量并不总是导致更高的预测性能。
An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customer's characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customer's characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set.The effectiveness of our proposal is confirmed by a comparison involving a real e-commerce transaction dataset and an artificial dataset; it generally achieves higher predictive performance. In addition, we show that preselecting the actual number of customer groups does not always lead to higher predictive performance.