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- Causal Inference (2)
- Clinical Epidemiology (2)
- Boosting; distance correlation; dose–response function; generalized propensity scores; high dimensional (1)
- Causal inference (1)
- Distance covariance; Hilbert-Schmidt independence criterion; Ker- 1 nel machine regression; Kernel distance covariance; Genetic association study; Score test; Permutation test. (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Estimating Controlled Direct Effects Of Restrictive Feeding Practices In The `Early Dieting In Girls' Study, Yeying Zhu, Debashis Ghosh, Donna L. Coffman, Jennifer S. Williams
Debashis Ghosh
In this article, we examine the causal effect of parental restrictive feeding practices on children’s weight status. An important mediator we are interested in is children’s self-regulation status. Traditional mediation analysis (Baron and Kenny, 1986) applies a structural equation modelling (SEM) approach and decomposes the intent-to-treat (ITT) effect into direct and indirect effects. More recent approaches interpret the mediation effects based on the potential outcomes framework. In practice, there often exist confounders that jointly influence the mediator and the outcome. Inverse probability weighting based on propensity scores are used to adjust for confounding and reduce the dimensionality of confounders simultaneously. …
Equivalence Of Kernel Machine Regression And Kernel Distance Covariance For Multidimensional Trait Association Studies, Wen-Yu Hua, Debashis Ghosh
Equivalence Of Kernel Machine Regression And Kernel Distance Covariance For Multidimensional Trait Association Studies, Wen-Yu Hua, Debashis Ghosh
Debashis Ghosh
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates …
A General Approach To Goodness Of Fit For U Processes, Debashis Ghosh, Youngjoo Cho
A General Approach To Goodness Of Fit For U Processes, Debashis Ghosh, Youngjoo Cho
Debashis Ghosh
Goodness of fit procedures are essential tools for assessing model adequacy in statistics. In this work, we present a general theory and approach to goodness of fit techniques based on U-processes for the accelerated failure time (AFT) model. Many of the examples will focus on U-statistics of order 2. While many authors have proposed goodness of fit tests for U-statistics of order one, less has been developed for higher order U-statistics. In this paper, we propose goodness of fit tests for U-statistics of order 2 by using theoretical results from Nolan and Pollard (1987) and Nolan and Pollard (1988). We …
A Boosting Algorithm For Estimating Generalized Propensity Scores With Continuous Treatments, Yeying Zhu, Donna L. Coffman, Debashis Ghosh
A Boosting Algorithm For Estimating Generalized Propensity Scores With Continuous Treatments, Yeying Zhu, Donna L. Coffman, Debashis Ghosh
Debashis Ghosh
In this article, we study the causal inference problem with a continuous treatment variable using propensity score-based methods. For a continuous treatment, the generalized propensity score is defined as the conditional density of the treatment-level given covariates (confounders). The dose–response function is then estimated by inverse probability weighting, where the weights are calculated from the estimated propensity scores. When the dimension of the covariates is large, the traditional nonparametric density estimation suffers from the curse of dimensionality. Some researchers have suggested a two-step estimation procedure by first modeling the mean function. In this study, we suggest a boosting algorithm to …