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Model‐based clustering and classification of functional data

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Functional Data Analysis
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Finite Mixture Models
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Spatial Functional Data Analysis
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Applied Functional Data Analysis: Methods and Case Studies
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A maximum likelihood methodology for clusterwise linear regression
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Comparing partitions
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Bayesian Inference for Finite Mixtures of Generalized Linear Models with Random Effects
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Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
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Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation
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Hierarchical Gaussian process mixtures for regression
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Curve prediction and clustering with mixtures of Gaussian process functional regression models
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Model-based clustering of time series in group-specific functional subspaces
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Model-based clustering and segmentation of time series with changes in regime
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Mixtures of linear regressions
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A study of logspline density estimation
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Simultaneous curve registration and clustering for functional data
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Mixtures of regressions with predictor-dependent mixing proportions
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Model-based clustering for multivariate functional data
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Mixtures of spatial spline regressions for clustering and classification
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Exploratory analysis of functional data via clustering and optimal segmentation
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A hidden process regression model for functional data description. Application to curve discrimination
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Model-based functional mixture discriminant analysis with hidden process regression for curve classification
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Time series modeling by a regression approach based on a latent process
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Time series clustering with ARMA mixtures
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A robust EM clustering algorithm for Gaussian mixture models
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Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
inferredcites →https://doi.org/10.1016/s0167-9473(03)00032-xopenalex-snapshot
Curves discrimination: a nonparametric functional approach
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Bayesian Modelling and Inference on Mixtures of Distributions
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A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle
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Semiparametric Regression
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Inference in model-based cluster analysis
inferredcites →https://doi.org/10.1023/a:1020779827503openalex-snapshot
Modeling with Mixtures of Linear Regressions
inferredcites →https://doi.org/10.1063/1.1477037openalex-snapshot
Penalized maximum likelihood for multivariate Gaussian mixture
inferredcites →https://doi.org/10.1080/00949650802590261openalex-snapshot
Fitting mixtures of linear regressions
inferredcites →https://doi.org/10.1080/00949655.2015.1109096openalex-snapshot
Unsupervised learning of regression mixture models with unknown number of components
inferredcites →https://doi.org/10.1080/01621459.1971.10482356openalex-snapshot
Objective Criteria for the Evaluation of Clustering Methods
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A New Approach to Estimating Switching Regressions
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Estimating Mixtures of Normal Distributions and Switching Regressions
inferredcites →https://doi.org/10.1080/01621459.1996.10476679openalex-snapshot
A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
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Semiparametric mixtures of regressions
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A RANDOM-EFFECTS MIXTURE MODEL FOR CLASSIFYING TREATMENT RESPONSE IN LONGITUDINAL CLINICAL TRIALS
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Model-based clustering and data transformations for gene expression data
inferredcites →https://doi.org/10.1093/bioinformatics/btl165openalex-snapshot
A Mixture model with random-effects components for clustering correlated gene-expression profiles
inferredcites →https://doi.org/10.1093/biomet/ass003openalex-snapshot
Componentwise classification and clustering of functional data
inferredcites →https://doi.org/10.1093/oso/9780198773917.003.0010openalex-snapshot
Regime switching with time-varying transition probabilities
inferredcites →https://doi.org/10.1109/34.865189openalex-snapshot
Assessing a mixture model for clustering with the integrated completed likelihood
inferredcites →https://doi.org/10.1109/34.990138openalex-snapshot
Unsupervised learning of finite mixture models
inferredcites →https://doi.org/10.1109/5.18626openalex-snapshot
A tutorial on hidden Markov models and selected applications in speech recognition
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cvpr.2003.1211378openalex-snapshot
Discovering clusters in motion time-series data
inferredcites →https://doi.org/10.1109/dicta.2009.88openalex-snapshot
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data
inferredcites →https://doi.org/10.1109/ijcnn.2009.5178921openalex-snapshot
A regression model with a hidden logistic process for feature extraction from time series
inferredcites →https://doi.org/10.1109/ijcnn.2011.6033590openalex-snapshot
Model-based clustering with Hidden Markov Model regression for time series with regime changes
inferredcites →https://doi.org/10.1109/ijcnn.2012.6252818openalex-snapshot
Mixture model-based functional discriminant analysis for curve classification
inferredcites →https://doi.org/10.1109/ijcnn.2013.6706758openalex-snapshot
Robust EM algorithm for model-based curve clustering
inferredcites →https://doi.org/10.1109/massp.1986.1165342openalex-snapshot
An introduction to hidden Markov models
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A new look at the statistical model identification
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An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression
inferredcites →https://doi.org/10.1109/tpami.2007.70775openalex-snapshot
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
inferredcites →https://doi.org/10.1111/1467-9469.00350openalex-snapshot
Unsupervised Curve Clustering using B‐Splines
inferredcites →https://doi.org/10.1111/1467-9868.00095openalex-snapshot
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
inferredcites →https://doi.org/10.1111/1467-9868.00297openalex-snapshot
Functional Linear Discriminant Analysis for Irregularly Sampled Curves
inferredcites →https://doi.org/10.1111/1467-9876.00136openalex-snapshot
A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
inferredcites →https://doi.org/10.1111/j.1467-842x.1992.tb01356.xopenalex-snapshot
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT
inferredcites →https://doi.org/10.1111/j.1541-0420.2012.01828.xopenalex-snapshot
Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02073.xopenalex-snapshot
Discriminant Analysis by Gaussian Mixtures
inferredcites →https://doi.org/10.1111/rssb.12009openalex-snapshot
Spatial Spline Regression Models
inferredcites →https://doi.org/10.1111/stan.12093openalex-snapshot
Spatial clustering of time series via mixture of autoregressions models and Markov random fields
inferredcites →https://doi.org/10.1142/s0219691313500033openalex-snapshot
CLUSTERING FUNCTIONAL DATA USING WAVELETS
inferredcites →https://doi.org/10.1145/312129.312198openalex-snapshot
Trajectory clustering with mixtures of regression models
inferredcites →https://doi.org/10.1162/neco_a_00892openalex-snapshot
A Universal Approximation Theorem for Mixture-of-Experts Models
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Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
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A Tutorial on MM Algorithms
inferredcites →https://doi.org/10.1198/016214503000189openalex-snapshot
Clustering for Sparsely Sampled Functional Data
inferredcites →https://doi.org/10.1201/b11038openalex-snapshot
Gaussian Process Regression Analysis for Functional Data
inferredcites →https://doi.org/10.1214/10-sts351openalex-snapshot
A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data
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Estimation and model selection for model-based clustering with the conditional classification likelihood
inferredcites →https://doi.org/10.1214/aos/1176324456openalex-snapshot
Penalized Discriminant Analysis
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
inferredcites →https://doi.org/10.1214/aos/1176346060openalex-snapshot
On the Convergence Properties of the EM Algorithm
inferredcites →https://doi.org/10.21236/ada454825openalex-snapshot
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
inferredcites →https://doi.org/10.2307/2529003openalex-snapshot
Clustering Methods Based on Likelihood Ratio Criteria
inferredcites →https://doi.org/10.2307/2529876openalex-snapshot
Random-Effects Models for Longitudinal Data
inferredcites →https://doi.org/10.2307/3316063openalex-snapshot
The historical functional linear model
inferredcites →https://doi.org/10.32614/rj-2016-021openalex-snapshot
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
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Bayesian mixtures of spatial spline regressions
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Model-based clustering of high-dimensional data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Penalized Model-Based Functional Clustering: A Regularization Approach via Shrinkage Methods
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