Variable selection for market basket analysis
Variable selection for market basket analysis
复制标题
市场篮子分析的变量选择
DOI：
10.1007/s00180-012-0315-3
复制
发表时间：
2013
影响因子：
1.3
通讯作者：
H. Hruschka
中科院分区：
文献类型：
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作者：
Katrin Dippold;H. Hruschka
通过解释性市场篮子分析获得的关于跨类别效应的结果可能是有偏差的，因为研究通常只调查零售类别中的一小部分(Chib等人。计量经济学进展，第16卷。市场营销中的计量经济学模型。Jai，阿姆斯特丹，第57-92页，2002年)。我们使用贝叶斯变量选择技术来确定多变量Logit模型中显著的跨类别效应。因此，我们实现了要估计的系数的减少，这大大减少了计算时间，从而允许考虑比大多数以前的研究更多的产品类别。除了类别数量的扩展，本文的第二个目的是了解不同变量选择算法在市场篮子分析中的能力。我们提出了三种不同的变量选择方法，并发现Geweke(当代贝叶斯计量经济学和统计学)的一种技术的改编。Wiley，Hoboken，2005)最符合市场篮子分析的要求，即大量的观察和跨类别效应。对于一个真实的数据集，我们表明：(1)只有一小部分可能的跨类别效应与零显著不同(我们的数据为三分之一)，(2)大多数这些效应表明互补性，(3)所考虑的产品类别的数量影响跨类别效应的显著。
Results on cross category effects obtained by explanatory market basket analyses may be biased as studies typically investigate only a small fraction of the retail assortment (Chib et al. in Advances in econometrics, vol 16. Econometric models in marketing. JAI, Amsterdam, pp 57–92, 2002). We use Bayesian variable selection techniques to determine significant cross category effects in a multivariate logit model. Hence, we achieve a reduction of coefficients to be estimated which decreases computation time heavily and thus allows to consider more product categories than most previous studies. Next to the extension of numbers of categories, the second purpose of this paper is to learn about the capabilities of different variable selection algorithms in the context of market basket analysis. We present three different approaches to variable selection and find that an adaptation of a technique by Geweke (Contemporary Bayesian econometrics and statistics. Wiley, Hoboken, 2005) meets the requirements of market basket analysis best, namely high numbers of observations and cross category effects. For a real data set, we show (1) that only a moderate fraction of possible cross category effects are significantly different from zero (one third for our data), (2) that most of these effects indicate complementarity and (3) that the number of considered product categories influences significances of cross category effects.