Pages that link to "Item:Q3224221"

The following pages link to A moving average Cholesky factor model in covariance modelling for longitudinal data (Q3224221):

Displaying 50 items.

• Robust variable selection in semiparametric mean-covariance regression for longitudinal data analysis (Q278635) (← links)
• Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions (Q830449) (← links)
• Simultaneous modelling of the Cholesky decomposition of several covariance matrices (Q873623) (← links)
• Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models (Q1623700) (← links)
• A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data (Q1654266) (← links)
• ARMA Cholesky factor models for the covariance matrix of linear models (Q1658394) (← links)
• A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data (Q1659029) (← links)
• Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts (Q2042529) (← links)
• Generalized partial linear models with nonignorable dropouts (Q2075033) (← links)
• Estimation of semi-varying coefficient models for longitudinal data with irregular error structure (Q2076102) (← links)
• Conditional generalized estimating equations of mean-variance-correlation for clustered data (Q2076146) (← links)
• Bayesian cumulative logit random effects models with ARMA random effects covariance matrix (Q2131882) (← links)
• Triangular angles parameterization for the correlation matrix of bivariate longitudinal data (Q2131908) (← links)
• Robust estimation in multivariate heteroscedastic regression models with autoregressive covariance structures using EM algorithm (Q2146464) (← links)
• GEE analysis in joint mean-covariance model for longitudinal data (Q2175604) (← links)
• A joint mean-correlation modeling approach for longitudinal zero-inflated count data (Q2180256) (← links)
• A robust joint modeling approach for longitudinal data with informative dropouts (Q2228227) (← links)
• Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data (Q2273189) (← links)
• A double varying-coefficient modeling approach for analyzing longitudinal observations (Q2274194) (← links)
• Parsimonious mean-covariance modeling for longitudinal data with ARMA errors (Q2287377) (← links)
• Modeling of the ARMA random effects covariance matrix in logistic random effects models (Q2324306) (← links)
• Bayesian joint semiparametric mean-covariance modeling for longitudinal data (Q2328676) (← links)
• Quantile estimations via modified Cholesky decomposition for longitudinal single-index models (Q2330530) (← links)
• Estimation and model identification of longitudinal data time-varying nonparametric models (Q2400821) (← links)
• Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data (Q2403399) (← links)
• Robust maximum \(L_q\)-likelihood estimation of joint mean-covariance models for longitudinal data (Q2418528) (← links)
• A moving average Cholesky factor model in joint mean-covariance modeling for longitudinal data (Q2441146) (← links)
• Unified Inference for Sparse and Dense Longitudinal Data in Time‐varying Coefficient Models (Q2965547) (← links)
• Dynamic Conditionally Linear Mixed Models for Longitudinal Data (Q3078930) (← links)
• A profile likelihood approach for longitudinal data analysis (Q3119827) (← links)
• On modelling mean-covariance structures in longitudinal studies (Q4455407) (← links)
• Adaptive robust estimation in joint mean–covariance regression model for bivariate longitudinal data (Q4639149) (← links)
• Robust estimation for the correlation matrix of multivariate longitudinal data (Q5033434) (← links)
• A Cholesky-based estimation for large-dimensional covariance matrices (Q5037036) (← links)
• Robust statistical inference for longitudinal data with nonignorable dropouts (Q5044086) (← links)
• Bayesian analysis of joint mean and covariance models for longitudinal data (Q5130547) (← links)
• Improved <i>k</i>th power expectile regression with nonignorable dropouts (Q5867698) (← links)
• Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data (Q5964276) (← links)
• Minimax estimation of a normal covariance matrix with the partial Iwasawa decomposition (Q5964283) (← links)
• Bayesian estimation for longitudinal data in a joint model with HPCs (Q6044813) (← links)
• Improved composite quantile regression and variable selection with nonignorable dropouts (Q6063736) (← links)
• Dynamic covariance estimation via predictive Wishart process with an application on brain connectivity estimation (Q6115549) (← links)
• Robust estimation via modified Cholesky decomposition for modal partially nonlinear models with longitudinal data (Q6141717) (← links)
• Robust semiparametric modeling of mean and covariance in longitudinal data (Q6579475) (← links)
• A nonstationary and non-Gaussian moving average model for solar irradiance (Q6626435) (← links)
• Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach (Q6627941) (← links)
• Bayesian semi-parametric modeling of covariance matrices for multivariate longitudinal data (Q6628401) (← links)
• A Covariate-Regulated Sparse Subspace Learning Model and Its Application to Process Monitoring and Fault Isolation (Q6631131) (← links)
• Modelling correlation matrices in multivariate data, with application to reciprocity and complementarity of child-parent exchanges of support (Q6665488) (← links)
• Ultra-high dimensional longitudinal quantile feature screening based on modified Cholesky decomposition (Q6670794) (← links)