A dual latent class unfolding model for two-way two-mode preference rating data

Vera, José Ramón Navarro; Macías, Rodrigo; Heiser, Willem J.

doi:10.1016/j.csda.2008.07.019

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…For instance, future maps might incorporate additional data on product attributes and consumer preferences, which could introduce latent dimensions of consumer satisfaction with product attribute quality (Tirunillai and Tellis 2014). Additionally, a partitioning of consumers could be introduced to specifically account for consumer heterogeneity (Vera et al 2009). Furthermore, recent work in the area of submarket detection such as that of France and Ghose (2016) could be integrated into DRMABS to test for submarket structures using sales and conversion data.…”

Section: Discussionmentioning

confidence: 99%

Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data

Ringel

Skiera

2016

Marketing Science

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I n large markets comprising hundreds of products, comprehensive visualization of competitive market structures can be cumbersome and complex. Yet, as we show empirically, reduction of the analysis to smaller representative product sets can obscure important information. Herein we use big search data from a productand price-comparison site to derive consideration sets of consumers that reflect competition between products. We integrate these data into a new modeling and two-dimensional mapping approach that enables the user to visualize asymmetric competition in large markets (>1 000 products) and to identify distinct submarkets. An empirical application to the LED-TV market, comprising 1,124 products and 56 brands, leads to valid and useful insights and shows that our method outperforms traditional models such as multidimensional scaling. Likewise, we demonstrate that big search data from product-and price-comparison sites provide higher external validity than search data from Google and Amazon.

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Section: Discussionmentioning

confidence: 99%

Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data

Ringel

Skiera

2016

Marketing Science

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“…The bootstrap approach (Hope, ) is a widely employed alternative procedure. Although this method has been employed in the context of latent class models for unfolding or multidimensional scaling (see Vera et al ., , for further details), it adds a lot to CPU time.…”

Section: The Algorithmmentioning

confidence: 99%

“…In a probabilistic context, mixture distribution formulations are the natural way to proceed. A dual latent class unfolding model for continuous rating data, with the assumption that within each homogeneous group or latent class the data are independently and normally distributed, was developed by Vera, Macías, and Heiser () (see also Vera, Macías, & Angulo, ; and Vera, Macías, & Heiser, ). In a general cross‐classified framework, this approach is of particularly interest when the number of row (column) categories is large, or when the data are sparse, as is the case in the present paper.…”

Section: Introductionmentioning

confidence: 99%

A latent class distance association model for cross‐classified data with a categorical response variable

Vera

Rooij

Heiser

2014

Brit J Math & Statis

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In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented.

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“…Latent block clustering methods have been proposed using a Poisson model, for example in information retrieval (Li & Zha, 2006), or for sequencing data (Witten, 2011), among others. With the aim of reducing the number of parameters and at the same time to facilitate the interpretation, clustering and representation methods have been proposed in different areas for different data sets (see, e.g., Kim, Choi, & Hwang, 2017;Vera, Mac ıas, & Heiser, 2009a, Vera, Mac ıas, & Heiser, 2009bVera, Mac ıas, & Heiser, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

Vera¹,

Rooij

2019

Multivariate Behavioral Research

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Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the model.

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A dual latent class unfolding model for two-way two-mode preference rating data

Cited by 14 publications

References 20 publications

Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data

Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data

A latent class distance association model for cross‐classified data with a categorical response variable

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

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