Variable Selection in Finite Mixture of Regression Models

By Abbas Khalili, Jiahua Chen. 2007

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/52b60325adc1d36bca9afdbdca9e8c091655f53a

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Citations in the corpus

Challenges of Big Data Analysis (2013, unknown): “Theyarebasedonexogeneityassumptionsthat oftencannotbevalidatedbycollecteddata,dueto incidentalendogeneity.”
Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences (2015, background): “…for fixed effects in linear and generalized linear mixed models (aka HLM or HGLM, respectively; Groll & Tutz, 2014; Park & Hastie, 2007; Schelldorfer, Meier, & Bühlmann, 2014) and also for mixture models with metric outcomes (Khalili & Chen, 2007; Städler, Bühlmann, & Van de Geer, 2010).”
ℓ1-penalization for mixture regression models (2010, background): “The value of γ = 1 has been proposed by Khalili and Chen (2007) for the naively parameterized likelihood from model (1).”
Slow‐growing species cope best with drought: evidence from long‐term measurements in a tropical semi‐deciduous moist forest of Central Africa (2013, methodology): “The finite mixture of regressions on two-level data with lasso penalization was fitted using an expectation-maximization algorithm (Dempster, Laird & Rubin 1977; Khalili & Chen 2007; see Appendix S4).”
The impact of big data on world-class sustainable manufacturing (2015, background): “The majority of studies so far have endeavored to understand the different dimensions of the concept and to capture the potential benefits to OM/SCM (Chen et al., 2013; Wamba et al., 2015).”
LEMNA: Explaining Deep Learning based Security Applications (2018, methodology): “Then, we integrate fused lasso into amixture regressionmodel [28] to approximate locally nonlinear decision boundaries to support complex security applications.”
Nonparametric modal regression (2014, background): “The literature on mixture regression, also known as mixture of experts modeling, is vast; see, e.g., Jacobs et al. (1991); Jiang and Tanner (1999); Bishop (2006); Viele and Tong (2002); Khalili and Chen (2007); Hunter and Young (2012); Huang and Yao (2012); Huang et al. (2013).”
Regression shrinkage and selection via the lasso: a retrospective (2011, unknown): “…yet more challenging in terms of computation and understanding the theoretical phenomena: potential applications are mixture regression models (Khalili and Chen, 2007; Städler et al., 2011), linear mixed effects models (Bondell et al., 2010; Schelldorfer et al., 2011) or missing data problems…”