Liero (2003) advised a consistent test for heteroscedasticity in nonparametric regression models, which is based on the L 2 -distance between the underlying and hypothetical variance function. This test is analogous to the one proposed by Dette and Munk (1998). Dette (2002), Zheng (2009) and Zhu et. Model checking for parametric single-index models: A dimension-reduction model-adaptive approach A model check must ther efor e be perf ormed. A practical ex- non-parametric model. NONPARAMETRIC MODEL CHECKS OF SINGLE-INDEX ASSUMPTIONS Samuel Maistre1;2;3 and Valentin Patilea1 1CREST (Ensai) 2Universit e de Lyon 3Universit e de Strasbourg Supplementary Material This supplementary material contains additional proofs, details and technical lemmas. S1 Additional proofs and details S1.1 Proof of Lemma A.1 Lemma A.1. Nonparametric Checks For Single-Index Models . By Winfried Stute and Li-xing Zhu. Abstract. In this paper we study goodness-of-fit testing of single-index models. The large sample behavior of certain score-type test statistics is investigated. As a by-product, we obtain asymptotically distributionfree maximin tests for a large class of local
Abstract Semiparametric single-index assumptions are convenient and widely used dimen\-sion reduction approaches that represent a compromise between the parametric and fully nonparametric models for regressions or conditional laws.
Model checking for parametric single-index models: a dimension reduction model-adaptive approach. 2007. "A new test for the parametric form of the variance function in non‐parametric regression," Journal of the Royal Statistical Society Series 2002. "Model Checks for Generalized Linear Models," Scandinavian Journal of Statistics 8 Semiparametric Single Index Models 8.1 Index Models A object of interest such as the conditional density f (y j x) or conditional mean E(y j x) is a single index model when it only depends on the vector x through a single linear combination x0 : Most parametric models are single index, including Normal regression, Logit, Probit, Tobit, Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covariates. This paper has three purposes. First, we suggest a partial dimension reduction adaptive-to-model testing procedure that can be omnibus against general global alternative models although it fully use the A dimension reduction-based model adaptive test is proposed which behaves like a local smoothing test as if the number of covariates was equal to the number of their linear combinations in the mean regression function, in particular, equal to 1 when the mean function contains a single index.
Abstract: The single-index model with an unknown link function is a gen ear model The bias problem in estimation is an important issue in nonparametric in ference but In this article, we have constructed a Cramйr-von Mises test to check.
Local smoothing testing based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. However, Abstract: The single-index model with an unknown link function is a generalized lin- The bias problem in estimation is an important issue in nonparametric in- In this article, we have constructed a Cramér-von Mises test to check the. Abstract: The single-index model with an unknown link function is a gen ear model The bias problem in estimation is an important issue in nonparametric in ference but In this article, we have constructed a Cramйr-von Mises test to check. This article presents new nonparametric tests for heteroscedasticity in nonlinear Checking Heteroscedasticity in Partially Linear Single-Index Models Using I dont know how to compare Semipar Single index with other models. of fully non-parametric models, partially linear models and index models. I'm pretty sure it will do what you are after but not absolutely - just check the vignette i guess.