Results 41 to 50 of about 87 (87)

Asymptotically efficient two-sample rank tests for modal directions on spheres [PDF]

open access: yes
A general class of optimal and distribution-free rank tests for the two-sample modal directions problem on (hyper-) spheres is proposed, along with an asymptotic distribution theory for such spherical rank tests.
Tsai, Ming-Tien
core  

Nonconcave penalized inverse regression in single-index models with high dimensional predictors [PDF]

open access: yes
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.
Zhu, Li-Ping, Zhu, Li-Xing
core  

Remarks on between estimator in the intraclass correlation model with missing data [PDF]

open access: yes
In this paper, we consider the between estimator under the intraclass correlation model with missing data. We give a necessary and sufficient condition for existing exact simultaneous confidence intervals for all contrasts in the means under the between ...
Yu, Kai-Fun, Wu, Mi-Xia
core  

Order restricted inference for sequential k-out-of-n systems [PDF]

open access: yes
Sequential order statistics have been introduced to model sequential k-out-of-n systems which, as an extension of k-out-of-n systems, allow the failure of some components of the system to influence the remaining ones.
Balakrishnan, N., Beutner, E., Kamps, U.
core  

Diagnostic checking for multivariate regression models [PDF]

open access: yes
Diagnostic checking for multivariate parametric models is investigated in this article. A nonparametric Monte Carlo Test (NMCT) procedure is proposed. This Monte Carlo approximation is easy to implement and can automatically make any test procedure scale-
Song, Song, Zhu, Lixing, Zhu, Ruoqing
core  

Signed-rank tests for location in the symmetric independent component model [PDF]

open access: yes
The so-called independent component (IC) model states that the observed p-vectorX is generated via X=[Lambda]Z+[mu], where [mu] is a p-vector, [Lambda] is a full-rank matrix, and the centered random vector Z has independent marginals.
Oja, Hannu   +2 more
core  

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