Extension of model-based classification for binary data when training and test populations differ
From MaRDI portal
Publication:5123570
DOI10.1080/02664760902889957OpenAlexW2130376054MaRDI QIDQ5123570
Julien Jacques, Christophe Biernacki
Publication date: 29 September 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664760902889957
Related Items
Adapting a classification rule to local and global shift when only unlabelled data are available, Model-based co-clustering for ordinal data, Sequential LND sensitivity test for binary response data
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Clustering criteria for discrete data and latent class models
- Estimating the dimension of a model
- Pairwise likelihood approach to grouped continuous model and its extension
- A Generalized Discriminant Rule When Training Population and Test Population Differ on Their Descriptive Parameters
- Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties
- Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients
- The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
- Separate sample logistic discrimination
- A new look at the statistical model identification