Longitudinal Data Regression Analysis Using Semiparametric Modelling
Revstat Statistical JournalZhang, Leng and Tang (2015) propose joint parametric modelling of the means, variances, and the correlations by decomposing the correlation matrix via hyperspherical co-ordinates and show that this results unconstrained parameterization, fast ...
Abdulla Mamun, Sudhir Paul
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VarReg: An R package for semi-parametric variance regression
SoftwareXVariance regression is used to model heteroscedasticity in terms of covariates. Algorithms already exist but they may face computational instability due to the required parameter constraints.
Kristy P Robledo, Ian C Marschner
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Maximum Likelihood Estimation for Semiparametric Regression Models With Panel Count Data. [PDF]
Biometrika, 2021 Zeng BD, Lin DY.
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Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials. [PDF]
Can J Stat, 2023 Sun Y, Heng F, Lee U, Gilbert PB.
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Semiparametric regression analysis of partly interval-censored failure time data with application to an AIDS clinical trial. [PDF]
Stat Med, 2021 Zhou Q, Sun Y, Gilbert PB.
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Semiparametric regression analysis of case-cohort studies with multiple interval-censored disease outcomes. [PDF]
Stat Med, 2021 Zhou Q, Cai J, Zhou H.
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Fuzzy Semi-Parametric Regression Based on Z-numbers
Transactions on Fuzzy Sets and SystemsSemi-parametric regression models are highly valued for combining parametric structure with nonparametric flexibility. However, in real-world situations involving data with inherent uncertainty and variable reliability, classical fuzzy numbers are ...
Mahdi Ali Abdulhussein +2 more
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PLoS ONEThe accurate monitoring of metabolic syndrome in older adults is relevant in terms of its early detection, and its management. This study aimed at proposing a novel semiparametric modeling for a cardiometabolic risk index (CMRI) and individual risk ...
Philippe Tagder +7 more
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The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data. [PDF]
J Appl Stat, 2022 Vasconcelos JCS +2 more
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Robust Estimation and Inference for Semiparametric and Nonparametric Regression Models
MathematicsParametric regression methods are efficient when correctly specified but are sensitive to model misspecification and outliers. Nonparametric regression offers greater flexibility at the cost of reduced interpretability and susceptibility to the curse of ...
Hamdy F. F. Mahmoud +2 more
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