Japanese Journal of Statistics and Data Science - Call for Papers on "Advances in Latent Variable Theory and Methodology"
Coordinating Editors: Hien Nguyen (La Trobe University, Australia / Kyushu University, Japan) and Kei Hirose (Kyushu University, Japan)
Due Date: 13 December, 2024
Intended Publication Date: 1 December, 2025
Description:
When measuring the outcome of some natural or human-made phenomena, it is impossible to capture all important characteristics of the underlying objects of interest. As such, measurements are typically incomplete, and general statistical models are often approximations of the underlying true relationships that characterize the phenomena of interest. When more precise and specific modelling is required, uncovering the underlying unobservable aspects of data, or the latent or hidden variables, often yields useful inference regarding the objects of interest. Examples of such methods include the modelling of heterogeneous population dynamics via finite mixture and hidden Markov models, the revelation of underlying explanatory attributes in factor analysis models and autoencoders, or the measurement of individual variability in repeated measurements data via random effects models and longitudinal models. Submissions that include interesting applications of state-of-the-art methods related to the above topics to real data are encouraged.
In this special issue, we celebrate the achievements of modern statisticians and data scientists who develop new tools and uncover new theories that permit inference making and modelling regarding underlying latent and hidden variables that influence the outcomes of observable data. We invite authors from across the statistics, machine learning, and data science disciplines to submit novel research that provides new methods for modelling latent variables, new inferential tools for testing, interpreting, and making predictions, and new theories that explain the efficacy and statistical foundations of existing and new models.
Topics of interest for this issue include but are not limited to:
- Autoencoders
- Cluster Analysis
- Discrete Choice Models
- Empirical Bayes Estimators
- Factor Analysis
- Finite Mixture Models
- Hidden Markov Models
- Item Response Theory Models
- Longitudinal Models
- Missing Data Imputation
- Mixed Effects Models
- Mixtures of Experts
- Neural Networks
- Ordinal Regression
- Partial Least Squares
- Principal Component Analysis
- Random Effects Models
- Small Area Estimation
- Structural Equation Modelling
Papers must be submitted to the journal's submission system. Please select “Yes” for the question “Does this manuscript belong to a special feature?” and then select the special feature “S.I. : Advances in Latent Variable Theory and Methodology”.
Unfortunately, it will take a while for the submission system to be ready to receive submissions for this special feature topic. Please refrain from submitting your paper until further notice will be made in this page. Please note that the system can receive other paper submissions (i.e., papers not intended for this special feature) as usual.
UPDATED on April 8, 2024: The submission system is now ready to receive submissions for "S.I. : Advances in Latent Variable Theory and Methodology".