arXiv subjects

Mariko Takagishi

Publications and source records attributed to Mariko Takagishi.

3 recordsLinked to original sources

arXiv2026

Visualizing Class Specific Heterogeneous Tendencies using R

In this paper we introduce the R package mccca, which implements multiple-class cluster correspondence analysis (MCCCA) proposed in M.Takagishi et al., (2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to ``classes'' (e.g., gender and nationality) in a low dimensional space. In MCCCA, two kinds of variables, external and active variables, are distinguished. External variables directly define classes whereas the active variables are used to derive class-specific clusters of observations (individuals). In this paper, we show how to apply MCCCA and how to visualize its results using mccca. We illustrate the procedure for performing MCCCA by applying it to two data sets.

stat.ME↗

arXiv2019

Visualizing class specific heterogeneous tendencies in categorical data

In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories or individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including categorical variables for which the interdependencies are not of immediate concern, but that facilitate the interpretation of the plot with respect to relationships between individuals and categories. This article proposes a new method for adding such information, according to a multiple-set cluster correspondence analysis approach that identifies clusters specific to classes, or subsets of the data that correspond to the categories of the additional variables. The proposed method can construct a biplot that depicts heterogeneous tendencies of individual members, as well as their relationships with the original categorical variables. A simulation study to investigate the performance of this proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach.

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arXiv2018

Bi-clustering for time-varying relational count data analysis

Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. Bi-clustering such relational count data reveals the latent structure of the relationship between objects such as household items or people. When relational count data observed at multiple time points are available, it is worthwhile incorporating the time structure into the bi-clustering result to understand how objects move between the cluster over time. In this paper, we propose two bi-clustering methods for analyzing time-varying relational count data. The first model, the dynamic Poisson infinite relational model (dPIRM), handles time-varying relational count data. In the second model, which we call the dynamic zero-inflated Poisson infinite relational model, we further extend the dPIRM so that it can handle zero-inflated data. Proposing both two models is important as zero-inflated data are often encountered, especially when the time intervals are short. In addition, by explicitly deriving the relevant full conditional distributions, we describe the features of the estimated parameters and, in turn, the relationship between the two models. We show the effectiveness of both models through a simulation study and a real data example.

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