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Shrinkage estimators for semiparametric regression model
, 2021 Semiparametric regression models are extensions of linear regression models to include a nonparametric function of some explanatory variables. In semiparametric regression model researchers often encounter the problem of multicollinearity. In the context
Hadi Mohammed, Z. Algamal
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Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Mathematics, 2022 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 PENALIZED SPLINE REGRESSION [PDF]
Bulletin of informatics and cybernetics, 2012 In this paper, we propose a new semiparametric regression estimator by using a hybrid technique of a parametric approach and a nonparametric penalized spline method. The overall shape of the true regression function is captured by the parametric part, while its residual is consistently estimated by the nonparametric part.
Yoshida, Takuma, Naito, Kanta
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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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Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data. [PDF]
Biometrika, 2017 Summary Interval‐censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time‐
Zeng D, Gao F, Lin DY.
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Real-Time Semiparametric Regression [PDF]
Journal of Computational and Graphical Statistics, 2014 We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. Our definition of semiparametric regression is quite broad and includes, as special cases, generalized linear mixed models, generalized additive models ...
Luts, Jan +2 more
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Nonparametric and semiparametric estimation with discrete regressors [PDF]
, 1994 This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems.
Delgado, Miguel A., Mora, Juan
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Bayesian variants of some classical semiparametric regression techniques [PDF]
, 2004 This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=zβ+f(x)+var epsilon where f(.) is an unknown function.
Koop, Gary, Poirier, Dale J.
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Mathematics, 2023 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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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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