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Multivariate semiparametric control charts for mixed-type data
Statistical Methods in Medical Research, 2023A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process.
Elisavet M Sofikitou +2 more
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Discovering Functional Dependencies from Mixed-Type Data
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020No description ...
Panagiotis Mandros +3 more
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Genetic algorithm for clustering mixed-type data
Journal of Electronic Imaging, 2011The k-modes algorithm was recently proposed to cluster mixed-type data. However, in solving clustering problems, the k-modes algorithm and its variants usually ask the user to provide the number of clusters in the data sets. Unfortunately, the number of clusters is generally unknown to the user. Therefore, clustering becomes a tedious task of trial-and-
Shiueng-Bien Yang, Yung-Gi Wu
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Complex Dimensionality Reduction: Ultrametric Models for Mixed-Type Data
2022The factorial latent structure of variables, if present, can be complex and generally identified by nested latent concepts ordered in a hierarchy, from the most specific to the most general one. This corresponds to a tree structure, where the leaves represent the observed variables and the internal nodes coincide with latent concepts defining the ...
marco mingione +2 more
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External Logistic Biplots for Mixed Types of Data
2020A simultaneous representation of individuals and variables in a data matrix is called a biplot. When variables are binary, nominal, or ordinal, a classical linear biplot representation is not adequate. Recently, biplots for categorical data-based logistic response models have been proposed.
Julio Cesar Hernandez-Sanchez +1 more
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Directional control schemes for processes with mixed-type data
International Journal of Production Research, 2015Mixed-type data consisting of both continuous observations and categorical observations are becoming prevalent in manufacturing processes and service management. The majority of existing statistical process control tools are designed to monitor either continuous data or categorical data but seldom both.
Ding, Dong, Tsung, Fu-gee, Li, Jian
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The quick dynamic clustering method for mixed-type data [PDF]
Automation and Remote Control, 2012 This paper describes a new approach to high-dimensional mixed-type data clustering with missing values, which combines information on common nearest neighbors with classic between-vectors distances calculated by an original technique. The results are applied to form intersecting clusters for every missing value.
M. B. Loginova +3 more
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Rank-based process control for mixed-type data
IIE Transactions, 2016ABSTRACTConventional statistical process control tools target either continuous or categorical data but seldom both at the same time. However, mixed-type data consisting of both continuous and categorical observations are becoming more common in modern manufacturing processes and service management.
Ding, Dong, Tsung, Fu-gee, Li, Jian
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State‐space models for multivariate longitudinal data of mixed types
Canadian Journal of Statistics, 1996AbstractWe propose a class of state‐space models for multivariate longitudinal data where the components of the response vector may have different distributions. The approach is based on the class of Tweedie exponential dispersion models, which accommodates a wide variety of discrete, continuous and mixed data.
Jørgensen, Bent +3 more
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