Cluster analysis of longitudinal profiles with subgroups

DOI10.1214/17-EJS1389zbMath1393.62032OpenAlexW2785880534MaRDI QIDQ1697471

Xiaolu Zhu, Annie Qu

Publication date: 20 February 2018

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1517367715

zbMATH Keywords

longitudinal datamodel selectionclusteringsplinepenalized regressionminimax concave penaltynonparametric spline method

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (14)

Clusterwise functional linear regression models ⋮ Conditional functional clustering for longitudinal data with heterogeneous nonlinear patterns ⋮ Individualized Multidirectional Variable Selection ⋮ Fusion learning of functional linear regression with application to genotype-by-environment interaction studies ⋮ Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging ⋮ Grouped Generalized Estimating Equations for Longitudinal Data Analysis ⋮ Identifying subgroups of age and cohort effects in obesity prevalence ⋮ clusterMLD: An Efficient Hierarchical Clustering Method for Multivariate Longitudinal Data ⋮ Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models ⋮ iFusion: Individualized Fusion Learning ⋮ Clustering of subsample means based on pairwise L1 regularized empirical likelihood ⋮ Longitudinal Principal Component Analysis With an Application to Marketing Data ⋮ Biclustering analysis of functionals via penalized fusion ⋮ Subgroup analysis for high-dimensional functional regression

Uses Software

• AS 136

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Algorithm AS 136: A K-Means Clustering Algorithm
• Nearly unbiased variable selection under minimax concave penalty
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data
• Local asymptotics for regression splines and confidence regions
• Asymptotic properties of penalized spline estimators
• Unsupervised Curve Clustering using B‐Splines
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Likelihood-Based Selection and Sharp Parameter Estimation
• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data
• A practical guide to splines.

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