In this paper we aim to estimate the direction in general single-index models and to select important variables simultaneously when a diverging number of predictors are involved in regressions. Towards this end, we propose the nonconcave penalized inverse regression method. Specifically, the...

In this paper, we consider a semiparametric modeling with multi-indices when neither the response nor the predictors can be directly observed and there are distortions from some multiplicative factors. In contrast to the existing methods in which the response distortion deteriorates estimation...

In this paper we aim to construct adaptive confidence region for the direction of [xi] in semiparametric models of the form Y=G([xi]TX,[epsilon]) where G([dot operator]) is an unknown link function, [epsilon] is an independent error, and [xi] is a pnx1 vector. To recover the direction of [xi],...

In this paper, we use the kernel method to estimate sliced average variance estimation (SAVE) and prove that this estimator is both asymptotically normal and root n consistent. We use this kernel estimator to provide more insight about the differences between slicing estimation and other...

This paper addresses the problem of testing for a multivariate distribution, which belongs to a known parametric distribution family. The estimated Crámer-Von Mises-type test statistics are constructed using projection pursuit technique. Some interested properties of the test statistics, like...

Motivated by a practical problem, [Z.W. Cai, P.A. Naik, C.L. Tsai, De-noised least squares estimators: An application to estimating advertising effectiveness, Statist. Sinica 10 (2000) 1231-1243] proposed a new regression model with noised variables due to measurement errors. In this model, the...

In this paper we propose a new approach for estimating the unknown parameter in the stochastic linear regressive model with stationary ergodic sequence of covariates. Under mild conditions on the joint distribution of the covariate and the error, the estimator constructed is shown to be strongly...

In this paper, we suggest the conditional test procedures for testing elliptical symmetry of multivariate distribution. The conditional tests are exactly valid if the symmetric center and the shape matrix are given and are asymptotically valid if they are unknowns to be estimated. The...

The paper presents some permutation test procedures for multivariate location. The tests are based on projected univariate versions of multivariate data. For one-sample cases, the tests are affine invariant and strictly distribution-free for the symmetric null distribution with elliptical...

The paper presents a permutation procedure for testing reflected (or diagonal) symmetry of the distribution of a multivariate variable. The test statistics are based in empirical characteristic functions. The resulting permutation tests are strictly distribution free under the null hypothesis...