Abstract
We propose a method to construct a joint statistical model for mixed-domain data to analyze their dependence. Multivariate Gaussian and log-linear models are particular examples of the proposed model. It is shown that the functional equation defining the model has a unique solution under fairly weak conditions. The model is characterized by two orthogonal parameters: the dependence parameter and the marginal parameter. To estimate the dependence parameter, a conditional inference together with a sampling procedure is proposed and is shown to provide a consistent estimator. Illustrative examples of data analyses involving penguins and earthquakes are presented.
Acknowledgements
We thank the AE and three referees for their constructive comments that have improved the quality of this paper. We thank Yici Chen, Hironori Fujisawa, Masayuki Kano, Hisahiko Kubo, Michiko Okudo, and Akifumi Okuno for helpful discussions. We used the earthquake catalog provided by the Japan Meteorological Agency (Japan Meteorological Agency (2022)) and used GMT software package (Wessel and Smith (1998)) to create the maps. This work is supported by JST CREST (JPMJCR1763), JSPS KAKENHI (19K20222, 21H05205, 21K11781, 21K12067), and MEXT (JPJ010217). The R and Python codes are available at https://doi.org/10.5281/zenodo.8012980.
Citation
Tomonari Sei. Keisuke Yano. "Minimum information dependence modeling." Bernoulli 30 (4) 2623 - 2643, November 2024. https://doi.org/10.3150/23-BEJ1687
Information
