Results 11 to 20 of about 6,656,167 (244)

Semiparametric Bayesian inference in multiple equation models [PDF]

, 2005
This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Poirier, Dale J., Tobias, Justin, Koop, Gary   +2 more
core   +4 more sources

Generalizing sample tree information with semiparametric and parametric models.

Silva Fennica, 1995
Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model.
Kangas, Annika, Korhonen, Kari
doaj   +1 more source

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, Taeryon Choi, Beomjo Park, Peter Lenk   +3 more
doaj   +1 more source

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
doaj   +1 more source

Semiparametric Integrated and Additive Spatio-Temporal Single-Index Models

Mathematics, 2023
In this paper, we introduce two semiparametric single-index models for spatially and temporally correlated data. Our first model has spatially and temporally correlated random effects that are additive to the nonparametric function, which we refer to as ...
Hamdy F. F. Mahmoud, Inyoung Kim
doaj   +1 more source

SEMIPARAMETRIC MULTIVARIATE VOLATILITY MODELS [PDF]

Econometric Theory, 2007
Summary: We consider a model for a multivariate time series where the conditional covariance matrix is a function of a finite-dimensional parameter and the innovation distribution is nonparametric. The semiparametric lower bound for the estimation of the Euclidean parameter is characterized, and it is shown that adaptive estimation without ...
Hafner, C.M., Rombouts, J.V.K.
openaire   +3 more sources

Semiparametric transition models [PDF]

Econometric Reviews, 2021
A new semiparametric time series model is introduced – the semiparametric transition (SETR) model – that generalizes the threshold and smooth transition models by letting the transition function to be of an unknown form. Estimation is based on a combination of the (local) least squares estimations of the transition function and regression parameters ...
Cizek, Pavel, Koo, Chao Hui
openaire   +2 more sources

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, Ammar Muslim Abdulhussein   +1 more
doaj   +1 more source

Penalized Likelihood-Based Estimation of Stratified Semiparametric Cox Models Under Partly Interval Censoring. [PDF]

Biom J
ABSTRACT In survival data analysis, the stratified Cox model becomes a popular option when the proportional hazards assumption of the conventional Cox model does not hold for certain covariates. For a stratified Cox model, when the observed survival times contain only right censoring, the method of maximum partial likelihood can still be implemented ...
Ma J, Webb A, Manuguerra M, Hudson HM.
europepmc   +2 more sources

Semiparametric modelling of multicategorical data [PDF]

Journal of Statistical Computation and Simulation, 2004
Parametric multicategorical models are an established tool in statistical data analysis. Alternative semi-parametric models are introduced where part of the explanatory variables is still linearly parametrized and the rest is given as a sum of unspecified functions of the explanatory variables.
Tutz, Gerhard, Scholz, T.
openaire   +1 more source