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Source: Computational Statistics and Data Analysis
Resulting in 2 citations.
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Heuchenne, Cedric Jacquemain, Alexandre |
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Inference for Monotone Single-index Conditional Means: A Lorenz Regression Approach Computational Statistics and Data Analysis published online (9 September 2021): DOI: 10.1016/j.csda.2021.107347. Also: https://www.sciencedirect.com/science/article/pii/S016794732100181X Cohort(s): Young Men Publisher: Elsevier Keyword(s): Educational Attainment; Monte Carlo; Regions; Statistical Analysis; Wages The Lorenz regression procedure quantifies the inequality of a response explained by a set of covariates. Formally, it gives a weight to each covariate to maximize the concentration index between the response and a weighted average of the covariates. The obtained index is called the explained Gini coefficient. Unlike methods based on decompositions of inequality measures, the procedure does not assume a linear relationship between the response and the covariates. Inference can be performed by noticing a similarity with the monotone rank estimator, introduced in the context of the single-index model. A continuity correction is presented in the presence of discrete covariates. The Lorenz-R2 is a goodness-of-fit measure evaluating the proportion of explained inequality and is used to build a test of joint significance of several covariates. Monte-Carlo simulations and a real-data example are presented. |
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Bibliography Citation
Heuchenne, Cedric and Alexandre Jacquemain. "Inference for Monotone Single-index Conditional Means: A Lorenz Regression Approach." Computational Statistics and Data Analysis published online (9 September 2021): DOI: 10.1016/j.csda.2021.107347.
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| 2. |
Lee, Jung Wun Chung, Hwan Jeon, Saebom |
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Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use Computational Statistics and Data Analysis 161 (September 2021): 107261. Also: https://www.sciencedirect.com/science/article/pii/S0167947321000955 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Bayesian; Depression (see also CESD); Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo; Statistical Analysis; Substance Use Multivariate latent class profile analysis (MLCPA) is a useful tool for exploring the stage-sequential process of multiple latent class variables, but the inference can be challenging due to the high-dimensional latent structure of the model. In this paper, a Bayesian approach via Markov chain Monte Carlo (MCMC) is proposed for MLCPA as an alternative to the maximum-likelihood (ML) method. Compared to the ML solution, Bayesian estimates are less sensitive to the set of initial values as well as easier to obtain standard errors. We also address issues in MCMC such as label-switching problem with a dynamic data-dependent prior and computational complexity with a recursive formula. Simulation studies revealed the validity and efficiency of the proposed algorithm. An empirical analysis of MLCPA using the National Longitudinal Survey of Youth 97 (NLSY97) identified a small number of representative developmental progressions of adolescent depression and substance use. |
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Bibliography Citation
Lee, Jung Wun, Hwan Chung and Saebom Jeon. "Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use." Computational Statistics and Data Analysis 161 (September 2021): 107261.
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