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Bayesian quantile semiparametric mixed-effects double regression models
Statistical Theory and Related Fields, 2021 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 Regression Pursuit. [PDF]
Stat Sin, 2012 The semiparametric partially linear model allows flexible modeling of covariate effects on the response variable in regression. It combines the flexibility of nonparametric regression and parsimony of linear regression. The most important assumption in the existing methods for the estimation in this model is to assume a priori that it is known which ...
Huang J, Wei F, Ma S.
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Semiparametric Additive Beta Regression Models
Revstat Statistical Journal, 2021 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
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022 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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Endogeneity in Semiparametric Threshold Regression [PDF]
SSRN Electronic Journal, 2017 This paper estimates threshold regression models with an endogenous threshold variable using a nonparametric control function approach. Assuming diminishing threshold effects, we derive the consistency and limiting distribution of our proposed estimator constructed from the series approximation method for weakly dependent data.
Kourtellos, Andros +2 more
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Fuzzy Semi-Parametric Logistic Quantile Regression Model
Wasit Journal for Pure Sciences, 2023 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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Semiparametric Tail Index Regression [PDF]
Journal of Business & Economic Statistics, 2020 Abstract–Understanding why extreme events occur is often of major scientific interest in many fields. The occurrence of these events naturally depends on explanatory variables, but there is a severe lack of flexible models with asymptotic theory for understanding this dependence, especially when variables can affect the outcome nonlinearly.
Rui Li, Chenlei Leng, Jinhong You
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Spline Semiparametric Regression Models
Journal of Kufa for Mathematics and Computer, 2015 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
Jurnal Ilmu Keuangan dan Perbankan, 2022 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
Journal of Statistical Software, 2019 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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