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- Functional Data Analysis (3)
- Causal Inference (2)
- Clinical Epidemiology (2)
- Statistical Models (2)
- Bayesian inference (1)
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- Bayesian inference; Function-on-function regression; Functional data analysis; Functional mixed models; Wavelet regression (1)
- 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)
- EQTL Analysis (1)
- Function-on-Function Regression (1)
- Functional Discriminant Analysis (1)
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- Functional data analysis (1)
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- Model combining (1)
- Multivariate Analysis (1)
- Random forests (1)
- Statistical Theory and Methods (1)
- Wavelet regression (1)
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Embアルゴリズムの新たな応用による多重比率補定(高橋将宜), Masayoshi Takahashi
Embアルゴリズムの新たな応用による多重比率補定(高橋将宜), Masayoshi Takahashi
Masayoshi Takahashi
No abstract provided.
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Jeffrey S. Morris
Medical and public health research increasingly involves the collection of more and more complex and high dimensional data. In particular, functional data|where the unit of observation is a curve or set of curves that are finely sampled over a grid -- is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data, presenting a simple model as well as a more extensive mixed model framework, along with multiple functional posterior …
Functional Regression, Jeffrey S. Morris
Functional Regression, Jeffrey S. Morris
Jeffrey S. Morris
Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the development of this field, which has accelerated in the past 10 years to become one of the fastest growing areas of statistics, fueled by the growing number of applications yielding this type of data. One unique characteristic of FDA is the need to combine information both across and within functions, which Ramsay and …
Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull
Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull
Jeffrey S. Morris
Current methods for conducting expression Quantitative Trait Loci (eQTL) analysis are limited in scope to a pairwise association testing between a single nucleotide polymorphism (SNPs) and expression probe set in a region around a gene of interest, thus ignoring the inherent between-SNP correlation. To determine association, p-values are then typically adjusted using Plug-in False Discovery Rate. As many SNPs are interrogated in the region and multiple probe-sets taken, the current approach requires the fitting of a large number of models. We propose to remedy this by introducing a flexible function-on-scalar regression that models the genome as a functional outcome. The …
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 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 …