Variable Selection for Market Basket Analysis

Market basket analysis; cross category effects; variable selection; multivariate logit model; pseudo likelihood estimation

Vascular Tissue Engineering: Effects of Integrating Collagen into a PCL Based Nanofiber Material

The engineering of vascular grafts is a growing field in regenerative medicine. Although numerous attempts have been made, the current vascular grafts made of polyurethane (PU), Dacron®, or Teflon® still display unsatisfying results. Electrospinning of biopolymers and native proteins has been in the focus of research to imitate the extracellular matrix (ECM) of vessels to produce a small caliber, off-the-shelf tissue engineered vascular graft (TEVG) as a substitute for poorly performing PU, Dacron, or Teflon prostheses. Blended poly-ε-caprolactone (PCL)/collagen grafts have shown promising results regarding biomechanical and cell supporting features. In order to find a suitable PCL/collagen blend, we fabricated plane electrospun PCL scaffolds using various collagen type I concentrations ranging from 5% to 75%. We analyzed biocompatibility and morphological aspects in vitro. Our results show beneficial features of collagen I integration regarding cell viability and functionality, but also adverse effects like the loss of a confluent monolayer at high concentrations of collagen. Furthermore, electrospun PCL scaffolds containing 25% collagen I seem to be ideal for engineering vascular grafts

A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

Based on market basket data, multicategory purchase incidence models analyze demand interdependencies between product categories. We propose a finite mixture multivariate logit model to derive segment-specific intercategory effects of market basket purchase. Under the assumption that only a fraction of intercategory effects are significant, we exclude irrelevant effects by variable selection. This leads to a detailed description of consumers' shopping behavior that varies over segments not only w.r.t. parameters' values but also w.r.t. included interaction effects. We find that a homogeneous model would overestimate the intensity of interaction between product categories

Compensatory versus noncompensatory models for predicting consumer preferences

Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007) to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes

Compensatory versus noncompensatory models for predicting consumer preferences

Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser \& Orlin, 2007; Kohli \& Jedidi, 2007) to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.Conjoint analysis, greedoid algorithm, choice modeling, lexicographic heuristics, noncompensatory heuristics, consumer choice, consumer preferences.