In this paper we study time-varying coefficient (beta coefficient) models with a time trend function to characterize the nonlinear, non-stationary and trending phenomenon in time series and to explain the behavior of asset returns. The general local polynomial method is developed to estimate the...

In this article, we propose a new canonical correlation method based on information theory. This method examines potential nonlinear relationships between px1 vector Y-set and qx1 vector X-set. It finds canonical coefficient vectors a and b by maximizing a more general measure, the mutual...

In this article, for the regression mean function of Y on , where Y is a scalar, is a px1 vector and W is a categorical variable, we propose a method, partial sparse MAVE, to achieve sufficient dimension reduction and variable selection on simultaneously. The method relaxes any particular...

We show that the test statistic for dimension in q-based principal Hessian directions (pHd) is distributed as a linear combination of [chi]2 random variables. Simpler distributions can result depending on the distribution of the predictors and the adequacy of the quadratic model.

Yin and Cook [2002. Dimension reduction for the conditional k-th moment in regression. J. Roy. Statist. Soc. B 64, 159-175] established a general equivalence between sliced inverse regression (sir) and a marginal moment method called Covk. In this note, we form a new marginal method called phdk...

Yin and Cook (J. Roy. Statist. Soc. Ser. B Part 2 64 (2002) 159) recently introduced a new dimension reduction method for regression called Covk. Here, we develop the asymptotic distribution of the Covk test statistic for dimension under weak assumptions. This serves as an analytic counterpart...

In this paper we propose a dimension reduction method for estimating the directions in a multiple-index regression based on information extraction. This extends the recent work of Yin and Cook [X. Yin, R.D. Cook, Direction estimation in single-index regression, Biometrika 92 (2005) 371-384] who...