Spline estimation of single-index models
SPLINE ESTIMATION OF SINGLE-INDEX MODELS Li Wang and Lijian Yang University of Georgia and Michigan State University Supplementary Material This note contains proofs for the main results. The following two propo-sitions play an important role in the proof. Proposition A.1 establishes the uniform convergence rate of the derivatives of ˆγ In this paper, based on a weakly dependent sample, we investigate a robust single-index model, where the single-index is identified by the best approximation to the multivariate prediction function of the response variable, regardless of whether the prediction function is a genuine single-index function. A polynomial spline estimator is (2013). Spline-based semiparametric estimation of partially linear Poisson regression with single-index models. Journal of Nonparametric Statistics: Vol. 25, No. 4, pp. 905-922. Estimation of Single-Index Models Based on Boosting Techniques Florian Leitenstorfer & Gerhard Tutz Ludwig-Maximilians-UniversitÄat MuncÄ hen Akademiestra¼e 1, 80799 MuncÄ hen ftutz, leiten g@stat.uni-muenchen.de June 19, 2008 Abstract In single-index models the link or response function is not considered as ¯xed. Abstract. We consider spline-based quasi-likelihood estimation for mixed Poisson regression with single-index models. The unknown smooth function is approximated by B-splines, and a modified Fisher scoring algorithm is employed to compute the estimates.The spline estimate of the nonparametric component is shown to achieve the optimal rate of convergence, and the asymptotic normality of the
SPLINE ESTIMATION OF SINGLE-INDEX MODELS. Li Wang and Lijian Yang. University of Georgia and Michigan State University. Abstract: For the past two
In this paper, based on a weakly dependent sample, we investigate a robust single-index model, where the single-index is identified by the best approximation to the multivariate prediction function of the response variable, regardless of whether the prediction function is a genuine single-index function. A polynomial spline estimator is (2013). Spline-based semiparametric estimation of partially linear Poisson regression with single-index models. Journal of Nonparametric Statistics: Vol. 25, No. 4, pp. 905-922. Estimation of Single-Index Models Based on Boosting Techniques Florian Leitenstorfer & Gerhard Tutz Ludwig-Maximilians-UniversitÄat MuncÄ hen Akademiestra¼e 1, 80799 MuncÄ hen ftutz, leiten g@stat.uni-muenchen.de June 19, 2008 Abstract In single-index models the link or response function is not considered as ¯xed. Abstract. We consider spline-based quasi-likelihood estimation for mixed Poisson regression with single-index models. The unknown smooth function is approximated by B-splines, and a modified Fisher scoring algorithm is employed to compute the estimates.The spline estimate of the nonparametric component is shown to achieve the optimal rate of convergence, and the asymptotic normality of the spline estimate increase to in nity with sample size. This motivates us to use smoothing splines for estimation in the single index model. This paper gives a systematic and rigorous study of a smoothing splines based estimator for the single index model under minimal assumptions and lls an important gap in the literature. The assumptions for m We propose penalized spline (P-spline) estimation of eta(0)((.))in partially linear single-index models, where the mean function has the form eta(0)(alpha(0)(T)x) + beta(0)(T)z.
SPLINE ESTIMATION OF SINGLE-INDEX MODELS Li Wang and Lijian Yang University of Georgia and Michigan State University Abstract: For the past two decades, the single-index model, a special case of pro-jection pursuit regression, has proven to be an efficient way of coping with the high-dimensional problem in nonparametric regression.
For the past two decades, the single-index model, a special case of pro- jection pursuit regression, has proven to be an efficient way of coping with the 30 Nov 2016 In contrast to the standard approach of using kernels or regression splines, we use smoothing splines to estimate the smooth link function. SPLINE ESTIMATION OF SINGLE-INDEX MODELS. Li Wang and Lijian Yang. University of Georgia and Michigan State University. Abstract: For the past two Single-index models are useful and fundamental tools for handling ''curse of dimen- sionality'' problems in nonparametric regression. Along with that, variable 31 Jan 2020 Volume 26, Number 2 (2020), 1587-1618. Efficient estimation in single index models through smoothing splines. Arun K. Kuchibhotla and Rohit K
Single-index models are useful and fundamental tools for handling ''curse of dimen- sionality'' problems in nonparametric regression. Along with that, variable
SPLINE ESTIMATION OF SINGLE-INDEX MODELS Li Wang and Lijian Yang University of Georgia and Michigan State University Supplementary Material This note contains proofs for the main results. The following two propo-sitions play an important role in the proof. Proposition A.1 establishes the uniform convergence rate of the derivatives of ˆγ For example, the single-index models assume that m (x) = g (x T θ 0). If the model is misspecified, i.e., m is not a genuine single-index function, the estimation of θ 0 might be biased and a goodness-of-fit test is often needed in this case. We propose a partially linear single-index proportional hazards regression model, which can model both linear and nonlinear covariate effects on the log hazard in the proportional hazards model. We adopt a polynomial spline smoothing technique to model the structured nonparametric single-index component for the nonlinear covariate effects.
For example, the single-index models assume that m (x) = g (x T θ 0). If the model is misspecified, i.e., m is not a genuine single-index function, the estimation of θ 0 might be biased and a goodness-of-fit test is often needed in this case.
SPLINE ESTIMATION OF SINGLE-INDEX MODELS Li Wang and Lijian Yang University of Georgia and Michigan State University Supplementary Material This note contains proofs for the main results. The following two propo-sitions play an important role in the proof. Proposition A.1 establishes the uniform convergence rate of the derivatives of ˆγ
10 Feb 2003 B-splines, Fisher-von Mises, projection pursuit regression, random walk. Metropolis. 1Corresponding author: Ian W . McKeague, Department of