Results 11 to 20 of about 6,488 (205)
Energies, 2020 We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three ...
Lu Yang, Shigeyuki Hamori
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
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
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
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
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
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
doaj +1 more source
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
Semi- and Nonparametric ARCH Processes
Journal of Probability and Statistics, 2011 ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models.
Oliver B. Linton, Yang Yan
doaj +1 more source
Semiparametric Regression in Capture–Recapture Modeling [PDF]
Biometrics, 2006 SummaryCapture–recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture–recapture models.
Gimenez, Olivier +4 more
openaire +3 more sources