Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data

DOI10.1111/j.0006-341X.2000.00394.xzbMath1060.62524OpenAlexW1967488761WikidataQ30600334 ScholiaQ30600334MaRDI QIDQ4667458

Jodi Lapidus, Joseph F. Powell, Edward J. Bedrick

Publication date: 20 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2000.00394.x

zbMATH Keywords

discriminant analysismultidimensional scalingmultivariate probit modeldistance between populations

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (11)

Fitting Manova Models with Missing Continuous or Ordinal Data Using Reference Priors ⋮ Distance Metrics and Clustering Methods for Mixed‐type Data ⋮ General mixed-data model: Extension of general location and grouped continuous models ⋮ Multivariate dynamic model for ordinal outcomes ⋮ A Hierarchical Bayesian Model for Spatial Prediction of Multivariate Non-Gaussian Random Fields ⋮ Regularization of the location model in discrimination with mixed discrete and continuous variables ⋮ A generalized Mahalanobis distance for mixed data ⋮ Prediction of Euclidean distances with discrete and continuous outcomes ⋮ Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data ⋮ Variable selection in discriminant analysis for mixed continuous-binary variables and several groups ⋮ Pairwise likelihood approach to grouped continuous model and its extension

Cites Work

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• Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
• Generalization of the Mahalanobis distance in the mixed case
• On the estimation of polychoric correlations and their asymptotic covariance matrix
• Estimation of polychoric correlation with elliptical latent variables
• Distance between populations using mixed continuous and categorical variables
• Discrimination and Classification Using Both Binary and Continuous Variables
• High-Dimensional Multivariate Probit Analysis
• The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
• Linear Statistical Inference and its Applications
• Multivariate Correlation Models with Mixed Discrete and Continuous Variables

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