The paper considers quantile-wavelet estimation for time-varying coefficients by embedding a wavelet kernel into quantile regression. Our methodology is quite general in the sense that we do not require unknown to be smooth curves of common degree or errors independently distributed. Quantile-wavelet robust outliers heavy-tailed data. model dynamic nonlinear time series. A strong Bahadur order ...

Abstract An important and widely used class of semiparametric models is formed by the varying-coefficient models. Although varying coefficients are traditionally assumed to be smooth functions, model considered here with coefficient functions containing a finite set discontinuities. Contrary existing nonparametric estimation piecewise under dependence applicable in time series heteroskedastic s...

This paper proposes a functional coefficient quantile regression model with heterogeneous and time-varying coefficients factor loadings. Estimation of the is done in two stages. First, we estimate unobserved common factors from linear exogenous covariates. Second, plug-in an affine transformation estimated to obtain model. The parameter estimators are consistent asymptotically normal. applicati...

Regression and autoregressive mixed models are classical used to analyze the relationship between time series response variable other covariates. The coefficients in traditional regression constants. However, for complicated data, of covariates may change with time. In this article, we propose a kind partial time-varying coefficient model obtain local weighted least-square estimators functions ...