Imagen de Portada

Estimating Broad Sense Heritability via Kernel Ridge Regression

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bley, Olivia, Lei, Elizabeth, Zhou, Andy, Shen, Xiaoxi
Formato:	Preprint
Publicado:	2024
Materias:	Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression
por: Kim, Seok-Jin
Publicado: (2026)

Transfer Learning of CATE with Kernel Ridge Regression
por: Kim, Seok-Jin, et al.
Publicado: (2025)

HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators
por: Song, Yanke, et al.
Publicado: (2024)

Generalized Ridge Regression: Biased Estimation for Multiple Linear Regression Models
por: Gómez, Román Salmerón, et al.
Publicado: (2024)

Non-null Shrinkage Regression and Subset Selection via the Fractional Ridge Regression
por: Park, Sihyung, et al.
Publicado: (2025)

Robust Genomic Prediction and Heritability Estimation using Density Power Divergence
por: Chowdhury, Upama Paul, et al.
Publicado: (2024)

Ridge Estimation of High Dimensional Two-Way Fixed Effect Regression
por: He, Junnan, et al.
Publicado: (2026)

Optimal Functional Bilinear Regression with Two-way Functional Covariates via Reproducing Kernel Hilbert Space
por: Yang, Dan, et al.
Publicado: (2023)

Lasso-Ridge Refitting: A Two-Stage Estimator for High-Dimensional Linear Regression
por: Liu, Guo
Publicado: (2025)

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
por: Wang, Kaizheng
Publicado: (2023)

Benchmarking Heritability Estimation Strategies Across 86 Configurations and Their Downstream Effect on Polygenic Risk Score Performance
por: Muneeb, Muhammad, et al.
Publicado: (2026)

Tractable Ridge Regression for Paired Comparisons
por: Varin, Cristiano, et al.
Publicado: (2024)

Bayesian Kernel Machine Regression via Random Fourier Features for Estimating Joint Health Effects of Multiple Exposures
por: Zhang, Danlu, et al.
Publicado: (2025)

Collapsible Kernel Machine Regression for Exposomic Analyses
por: McGee, Glen, et al.
Publicado: (2024)

Conditional-Marginal Nonparametric Estimation for Stage Waiting Times from Multi-Stage Models under Dependent Right Censoring
por: Zhang, Xiaoxi, et al.
Publicado: (2025)

Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
por: Shen, Andy A., et al.
Publicado: (2025)

Generalized Ridge Regression: Applications to Nonorthogonal Linear Regression Models
por: Gómez, Román Salmerón, et al.
Publicado: (2025)

Transfer Learning for Kernel-based Regression
por: Wang, Chao, et al.
Publicado: (2023)

Kernel Smoothing for Bounded Copula Densities
por: Muia, Mathias N., et al.
Publicado: (2025)

Transfer Learning for Moderate-Dimensional Ridge-Regularized Robust Linear Regression
por: Lyu, Lingfeng, et al.
Publicado: (2026)

Bayesian Image-on-Image Regression via Deep Kernel Learning based Gaussian Processes
por: Ma, Guoxuan, et al.
Publicado: (2023)

Kernel Three Pass Regression Filter
por: Jat, Rajveer, et al.
Publicado: (2024)

Robust Local Polynomial Regression with Similarity Kernels
por: Shulman, Yaniv
Publicado: (2025)

Wasserstein Regression with Empirical Measures and Density Estimation for Sparse Data
por: Zhou, Yidong, et al.
Publicado: (2023)

Ridge Regularized Estimation of VAR Models for Inference
por: Ballarin, Giovanni
Publicado: (2021)

Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
por: Wang, Caixing, et al.
Publicado: (2024)

High-dimensional Asymptotics of Generalization Performance in Continual Ridge Regression
por: Zhao, Yihan, et al.
Publicado: (2025)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
por: Allerbo, Oskar
Publicado: (2023)

Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification
por: Dai, Xiaowu, et al.
Publicado: (2024)

The Effective Number of Parameters in Kernel Density Estimation
por: Guglielmini, Sofia, et al.
Publicado: (2024)

Estimation of Treatment Effects based on Kernel Matching
por: Ding, Chong, et al.
Publicado: (2025)

Generalised Exponential Kernels for Nonparametric Density Estimation
por: Craig, Laura M., et al.
Publicado: (2026)

Causal Effect Estimation on Restricted Mean Survival Time in Case-Cohort Studies via a Matching Design
por: Ni, Andy, et al.
Publicado: (2026)

Generalized Local Polynomial Regression with Decomposed Context-Aware Kernels
por: Shulman, Yaniv
Publicado: (2026)

Simultaneous Sieve Estimation and Inference for Time-Varying Nonlinear Time Series Regression
por: Ding, Xiucai, et al.
Publicado: (2025)

Graph-based Square-Root Estimation for Sparse Linear Regression
por: Li, Peili, et al.
Publicado: (2024)

Variable Selection with Broken Adaptive Ridge Regression for Interval-Censored Competing Risks Data
por: Mahmoudi, Fatemeh, et al.
Publicado: (2025)

Counterfactual Density Estimation using Kernel Stein Discrepancies
por: Martinez-Taboada, Diego, et al.
Publicado: (2023)

Towards Semiparametric Bandwidth Selectors for Kernel Density Estimators
por: Hjort, Nils Lid
Publicado: (2026)

Quick and Simple Kernel Differential Equation Regression Estimators for Data with Sparse Design
por: Ge, Chunlei, et al.
Publicado: (2024)