Sparse common and distinctive covariates regression

Park, Soogeun; Ceulemans, Eva; Deun, Katrijn Van

doi:10.1002/cem.3270

Cited by 8 publications

(4 citation statements)

References 34 publications

(56 reference statements)

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“…A method for using multi-block data related to SPCR has also been proposed. [28][29][30] This method considers a penalized simultaneous minimization problem of the loss function of the regression and of the PCA. SPCR is a method using the least squares criterion, while Hirose and Imada proposed sparse factor regression with penalized likelihood.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Yuki

Tanioka

Yadohisa

2022

Statistics in Medicine

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We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

“…Qi et al proposed JSPCR, 27 an extension of SPCR, to be applied to the outcomes of an outlier. A method for using multi‐block data related to SPCR has also been proposed 28‐30 . This method considers a penalized simultaneous minimization problem of the loss function of the regression and of the PCA.…”

Section: Introductionmentioning

confidence: 99%

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Yuki

Tanioka

Yadohisa

2022

Statistics in Medicine

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show abstract

“…In this method, the principal components that contribute to the regression analysis can be estimated by simultaneously performing sparse principal component analysis and a generalized linear model based on Lasso. A method is also proposed for multi-block data with a similar objective function (Gvaladze et al, 2021;Van Deun et al, 2018;Park et al, 2021). However, these methods are for single outcome and have not been used in a framework for estimating treatment effects.…”

Section: Introductionmentioning

confidence: 99%

Estimation and visualization of treatment effects for multiple outcomes

Yuki¹,

Tanioka²,

Yadohisa³

2021

Preprint

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We consider a randomized controlled trial between two groups. The objective is to identify a population with characteristics such that the test therapy is more effective than the control therapy. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying interactions between treatments and covariates. To date, many methods have been proposed to identify subgroups for a single outcome. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we propose a multivariate regression method that introduces latent variables to estimate the treatment effect on multiple outcomes simultaneously. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The framework of the generalized linear model makes it applicable to various types of outcomes. Interpretation of subgroups is made by visualizing treatment effects and latent variables. This allows us to identify subgroups with characteristics that make the test therapy more effective for multiple outcomes. Simulation and real data examples demonstrate the effectiveness of the proposed method.

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“…Thus, SGCCA is of interest to our paper. Although it is able to deal with the HDLSS setting, SGCCA has been found to perform poorly in recovering the underlying structure of the components(Park, Ceulemans, & Van Deun, 2021;Vervloet, Van Deun, Van den Noortgate, & Ceulemans, 2016).Recently, Integrated Generalized Structural Component Analysis (IGSCA) was proposed byHwang et al (2021) as a one-stage method that can analyze models with both components and factors simultaneously. It contains three submodels: measurement, structural, and weighted relation models.…”

mentioning

confidence: 99%

Exploratory Structural Equation Modeling and the Curse of Dimensionality: A Regularized Least-squares Approach

Lê,

Vermunt,

Van Deun

2024

Preprint

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Existing latent variable methods are found to fail or provide unstable solutions when the number of variables is large compared to the sample size. To tackle this issue, we propose a two-stage regularized least-squares approach for exploratory structural equation modeling. In the first stage, we introduce a novel exploratory factor analysis technique that not only estimates the measurement model but also the factor scores; indeterminacy of the measurement model is addressed by imposing simple structure through regularizing techniques. The factor scores can then be used to estimate the structural model in the second stage. An extensive simulation shows that the proposed method outperforms other approaches in recovering the underlying sparse structure of the measurement model in both low-dimension high-sample-size and high-dimension low-sample-size settings. It also estimates the path coefficients of the structural model with low bias. An implementation of the proposed method in the R software is publicly available: https://github.com/trale97/RegularizedLSLV.

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Sparse common and distinctive covariates regression

Cited by 8 publications

References 34 publications

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Estimation and visualization of treatment effects for multiple outcomes

Exploratory Structural Equation Modeling and the Curse of Dimensionality: A Regularized Least-squares Approach

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