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Bayesian quantile semiparametric mixed-effects double regression models
Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang +3 more
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Semiparametric Additive Beta Regression Models
In this paper, we study a semiparametric additive beta regression model using a parameterization based on the mean and a dispersion parameter. This model is useful for situations where the response variable is continuous and restricted to the unit ...
Germán Ibacache-Pulgar +2 more
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Investigation of Parametric, Non-Parametric and Semiparametric Methods in Regression Analysis
Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods.The parametric regression method ...
Esra Yavuz, Mustafa Şahin
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Fuzzy Semi-Parametric Logistic Quantile Regression Model
In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models.
Ahmed Razzaq, Ayad H. shemaila
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Spline Semiparametric Regression Models
In this paper, we study semiparametric regression models with spline smoothing, and determining the numbers of knots and their locations by using some statistical criteria, a simulation model has been performed.
Ameera Jaber Mohaisen +1 more
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Analysis of The Debtor's Endurance using Cox Regression Semiparametric Method
The aim of this research was conducted to determine the factors that influence the resilience of car loan debtors in an area. The research method used is semiparametric Cox regression on secondary data, WAREHOUSE consisting of the customer profile ...
Vitri Aprilla Handayani* +3 more
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bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors
The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set
Seongil Jo +3 more
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Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health.
Yunquan Song, Yaqi Liu, Hang Su
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Semiparametric Regression Analysis via Infer.NET
We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models.
Jan Luts +3 more
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Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases.
I. Gede Nyoman Mindra Jaya +5 more
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