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 proposed for the single-index coefficients, and is shown to be root-n consistent and asymptoti-cally normal. Models with single-index structures are among the many existing popular semiparametric approaches for either the conditional mean or the conditional variance. This paper focuses on a single-index model for the conditional quantile. II.9 Regression Models with Heteroscedastic Errors If assumption (39) (homoscedastic errors) is violated, one has to deal with heteroscedastic errors, i.e. the variance differs among the Single-index models are similar to the first step of projection pursuit regression, a dimension-reduction method. In both cases the orientation vector can be estimated root-n consistently, even if the unknown univariate function (or nonparametric link function) is assumed to come from a large smoothness class.
We consider a single-index structure to study heteroscedasticity in regression with and phrases: Conditional variance, heteroscedasticity, single-index model ,
Heteroscedasticity checks for single index models. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Ma and Zhu (2013) studied the efficient estimators for the heteroscedastic partially linear single-index models allowing high dimensional covariates by constructing a semiparametrically efficient score function. Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high dimensional covariates Yanyuan Ma Texas A&M University, College Station, USA and Liping Zhu Shanghai University of Finance and Economics, People’s Republic of China [Received November 2010. Final revision May 2012] Summary. Doubly robust and efficient estimators for heteroscedastic partially linear single‐index models allowing high dimensional covariates. Yanyuan Ma. Texas A&M University, College Station, USA. Search for more papers by this author. Liping Zhu. Shanghai University of Finance and Economics, People's Republic of China.
II.9 Regression Models with Heteroscedastic Errors If assumption (39) (homoscedastic errors) is violated, one has to deal with heteroscedastic errors, i.e. the variance differs among the
13 Oct 2015 3) is a single index model or partially linear single index model, the corresponding number of the index becomes one or two, respectively. In this Theorem 5 can be easily generalized to show that ˜θ is an efficient estimator of θ0 under this specific heteroscedastic structure. Remark 3. The asymptotic variance
To test heteroscedasticity in single index models, in this paper two test statistics are proposed via quadratic conditional moments. Without the use of dimension
In this paper, we developed a single-index regression model with a functional errors' term that serves in checking the heteroscedasticity. Since the efficient Key words and phrases: Conditional variance, heteroscedasticity, single-index model, volatility. 1. Introduction. Many scientific studies rely on understanding the
Single-index models are similar to the first step of projection pursuit regression, a dimension-reduction method. In both cases the orientation vector can be estimated root-n consistently, even if the unknown univariate function (or nonparametric link function) is assumed to come from a large smoothness class.
ABSTRACTWe propose a new algorithm for simultaneous variable selection and parameter estimation for the single-index quantile regression (SIQR) model . 24 May 2006 Two estimation problems for single-index models are intensively discussed in We consider a heteroscedastic single-index regression model. 15 Jul 2015 One attractive feature of this model is its ability to capture the nonlinearity to study heteroscedasticity in a single-index regression model with.