Cover Image

Omitted covariates bias and finite mixtures of regression models for longitudinal responses

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alfo', Marco, Rocci, Roberto
Format:	Preprint
Published:	2026
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

lqmix: an R package for longitudinal data analysis via linear quantile mixtures
by: Alfó, Marco, et al.
Published: (2023)

A multiple imputation approach to distinguish curative from life-prolonging effects in the presence of missing covariates
by: Cipriani, Marta, et al.
Published: (2024)

Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach
by: Cipriani, Marta, et al.
Published: (2025)

Nonparametric regression for a circular response with error-in-covariate
by: Woolsey, Nicholas, et al.
Published: (2025)

Asymmetric Laplace distribution regression model for fitting heterogeneous longitudinal response
by: Barbieri, Antoine, et al.
Published: (2025)

Adjusting for bias due to measurement error in functional quantile regression models with error-prone functional and scalar covariates
by: Chen, Xiwei, et al.
Published: (2024)

A Dynamic Latent Space Model for Healthcare Mobility Networks: the Italian National Health Service case
by: Manente, Cecilia, et al.
Published: (2026)

Mean regression for (0,1) responses via beta scale mixtures
by: Otto, Arno, et al.
Published: (2026)

A flexible parametric approach to synthetic patients generation using health data
by: Cipriani, Marta, et al.
Published: (2024)

A tree perspective on stick-breaking models in covariate-dependent mixtures
by: Horiguchi, Akira, et al.
Published: (2022)

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models
by: López-Cheda, Ana, et al.
Published: (2024)

A covariate-dependent Cholesky decomposition for high-dimensional covariance regression
by: Kim, Rakheon, et al.
Published: (2026)

Convex estimation of Gaussian graphical regression models with covariates
by: Liu, Ruobin, et al.
Published: (2024)

Bayesian mixture modeling using a mixture of finite mixtures with normalized inverse Gaussian weights
by: Iwashige, Fumiya, et al.
Published: (2025)

Density regression via Dirichlet process mixtures of normal structured additive regression models
by: Rodríguez-Álvarez, María Xosé, et al.
Published: (2024)

Model-based clustering for covariance matrices via penalized Wishart mixture models
by: Cappozzo, Andrea, et al.
Published: (2024)

Nonparametric contaminated Gaussian mixture of regressions
by: Skhosana, Sphiwe B., et al.
Published: (2026)

A general joint latent class model of longitudinal and survival data with the covariance modelling
by: Miao, Ruoyu, et al.
Published: (2025)

A lack-of-fit test for quantile regression models with high-dimensional covariates
by: Conde-Amboage, Mercedes, et al.
Published: (2015)

Omitted-Variable Sensitivity Analysis for Generalizing Randomized Trials
by: Asiaee, Amir, et al.
Published: (2026)

Correction of estimator bias in linear regression with categorical covariates with classification error
by: Dias, Alexandre Garcia, et al.
Published: (2025)

The Effect of Omitted Variables on the Sign of Regression Coefficients
by: Masten, Matthew A., et al.
Published: (2022)

Finite mixture copulas for modeling dependence in longitudinal count data
by: Chattopadhyay, Subhajit
Published: (2024)

Deep mixture of linear mixed models for complex longitudinal data
by: Kock, Lucas, et al.
Published: (2023)

Generalized mixtures of finite mixtures and telescoping sampling
by: Frühwirth-Schnatter, Sylvia, et al.
Published: (2020)

Partial membership models for soft clustering of multivariate football player performance data
by: Seri, Emiliano, et al.
Published: (2024)

Exploring the validity of the complete case analysis for regression models with a right-censored covariate
by: Ashner, Marissa C., et al.
Published: (2023)

Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates
by: Sun, Zhuowei, et al.
Published: (2023)

Identifying Brexit voting patterns in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects
by: Berrettini, Marco, et al.
Published: (2021)

Assessing Omitted Variable Bias when the Controls are Endogenous
by: Diegert, Paul, et al.
Published: (2022)

Sandwich regression for accurate and robust estimation in generalized linear multilevel and longitudinal models
by: Young, Elliot H., et al.
Published: (2024)

A new block covariance regression model and inferential framework for massively large neuroimaging data
by: Kim, Hyoshin, et al.
Published: (2025)

Handling cellwise outliers by sparse regression and robust covariance
by: Raymaekers, Jakob, et al.
Published: (2019)

Quantifying Omitted Variable Bias in Nonlinear Instrumental Variable Estimators
by: Yen, Yu-Min
Published: (2026)

Rank-based Bayesian clustering via covariate-informed Mallows mixtures
by: Eliseussen, Emilie, et al.
Published: (2023)

A discomfort-informed adaptive Gibbs sampler for finite mixture models
by: Fabbrico, Davide, et al.
Published: (2025)

Feature aware covariance estimation, with application to mixtures of chemical exposures
by: Bersson, Elizabeth, et al.
Published: (2025)

Nonparametric regression of spatio-temporal data using infinite-dimensional covariates
by: Roy, Subhrajyoty, et al.
Published: (2026)

High-dimensional covariance regression with application to co-expression QTL detection
by: Kim, Rakheon, et al.
Published: (2024)

Bayesian estimation of covariate assisted principal regression for brain functional connectivity
by: Park, Hyung G.
Published: (2023)