Entering the new century, extreme weather, carbon emissions, and public health safety have become major threats to human survival. However, China has a large population and a fragile ecological environment, especially the extremely arid climate in the northwest region. Causing environmental...

Linearity, sometimes jointly with constant variance, is routinely assumed in the context of sufficient dimension reduction. It is well understood that, when these conditions do not hold, blindly using them may lead to inconsistency in estimating the central subspace and the central mean...

type="main" xml:id="rssb12044-abs-0001" <title type="main">Summary</title> <p>We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the...</p>

type="main" xml:id="sjos12067-abs-0001" <title type="main">Abstract</title>We study estimation and hypothesis testing in single-index panel data models with individual effects. Through regressing the individual effects on the covariates linearly, we convert the estimation problem in single-index panel data models to that...

Classical sufficient dimension reduction methods are sensitive to outliers present in predictors, and may not perform well when the distribution of the predictors is heavy-tailed. In this paper, we propose two robust inverse regression methods which are insensitive to data contamination:...

Generalized single-index models are natural extensions of linear models and circumvent the so-called curse of dimensionality. They are becoming increasingly popular in many scientific fields including biostatistics, medicine, economics and financial econometrics. Estimating and testing the model...