Penalized estimation in finite mixture of ultra-high dimensional regression models
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Publication:5095987
DOI10.1080/03610926.2020.1851717OpenAlexW3108431481MaRDI QIDQ5095987
Publication date: 12 August 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2020.1851717
EM algorithmvariable selectionorder selectionfinite mixture of regression modelsultra-high dimensional regression
Uses Software
Cites Work
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- Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
- A maximum likelihood methodology for clusterwise linear regression
- \(\ell_{1}\)-penalization for mixture regression models
- Choosing initial values for the EM algorithm for finite mixtures
- Consistent estimation of a mixing distribution
- A Markov model for switching regressions
- Identifiability of models for clusterwise linear regression
- Maximum smoothed likelihood estimation for a class of semiparametric Pareto mixture densities
- Nonconcave penalized likelihood with a diverging number of parameters.
- Finite mixture and Markov switching models.
- Strong oracle optimality of folded concave penalized estimation
- Large sample properties of the smoothly clipped absolute deviation penalized maximum likelihood estimation on high dimensions
- Model Selection for Gaussian Mixture Models
- Minimum Hellinger Distance Estimation for Finite Mixtures of Poisson Regression Models and Its Applications
- Variable Selection in Finite Mixture of Regression Models
- Estimating Mixtures of Normal Distributions and Switching Regressions
- Mixed Poisson Regression Models with Covariate Dependent Rates
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Finite mixture models
- Hypothesis Testing in Mixture Regression Models
- Penalized minimum‐distance estimates in finite mixture models
- A new model selection procedure for finite mixture regression models
- Smoothly Clipped Absolute Deviation on High Dimensions
- Order Selection in Finite Mixture Models With a Nonsmooth Penalty
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