Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial...

Existing model-free tests of the conditional coordinate hypothesis in sufficient dimension reduction (Cook (1998) [3]) focused mainly on the first-order estimation methods such as the sliced inverse regression estimation (Li (1991) [14]). Such testing procedures based on quadratic inference...

We revisit cumulative slicing estimation (CUME; Zhu et al., 2010) from a different perspective to gain more insights, then refine its performance by incorporating the intra-slice covariances. We also prove that our new method, under some conditions, is more comprehensive than CUME.

In order to curb anthropogenic carbon emissions and achieve the carbon intensity reduction target in China, it is crucial to shed light on influencing factors of carbon emissions at the city level. This paper selects Tianjin, one of the largest economic centers in northern China, as a study...

We propose two substantive extensions to the saddlepoint-based bootstrap (SPBB) methodology, whereby inference in parametric models is made through a monotone quadratic estimating equation (QEE). These are motivated through the first-order moving average model, where SPBB application is...

One way of estimating a function from indirect, noisy measurements is to regularise an inverse of its Fourier transformation, using properties of the adjoint of the transform that degraded the function in the first place. It is known that when the function is smooth, this approach can perform...

In this paper, we presented a theoretical result and then discussed possible applications of our result to SDR problems. In addition to providing insights into existing SDR methods when Y is univariate; our theorem also applies to multivariate responses, especially when the response takes the...

The requirement of constant censoring parameter [beta] in Koziol-Green (KG) model is too restrictive. When covariates are present, the conditional KG model (Veraverbekea and Cadarso-Suárez, 2000) which allows [beta] to be dependent on the covariates is more realistic. In this paper, using...