| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
A scale space approach for exploring structure in spherical data
|
Vuollo, Ville
|
|
2018
|
125 |
C |
p. 57-69
|
article
|
| 2 |
Continuum directions for supervised dimension reduction
|
Jung, Sungkyu
|
|
2018
|
125 |
C |
p. 27-43
|
article
|
| 3 |
Editorial Board
|
|
|
2018
|
125 |
C |
p. ii-iv
|
article
|
| 4 |
Identification of local sparsity and variable selection for varying coefficient additive hazards models
|
Qu, Lianqiang
|
|
2018
|
125 |
C |
p. 119-135
|
article
|
| 5 |
Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics
|
Alam, Md. Ashad
|
|
2018
|
125 |
C |
p. 70-85
|
article
|
| 6 |
Joint regression analysis of mixed-type outcome data via efficient scores
|
Marchese, Scott
|
|
2018
|
125 |
C |
p. 156-170
|
article
|
| 7 |
On the sample mean after a group sequential trial
|
Berckmoes, Ben
|
|
2018
|
125 |
C |
p. 104-118
|
article
|
| 8 |
Point process models for novelty detection on spatial point patterns and their extremes
|
Luca, Stijn E.
|
|
2018
|
125 |
C |
p. 86-103
|
article
|
| 9 |
Robust template estimation for functional data with phase variability using band depth
|
Cleveland, Jason
|
|
2018
|
125 |
C |
p. 10-26
|
article
|
| 10 |
Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data
|
Wang, Shuying
|
|
2018
|
125 |
C |
p. 1-9
|
article
|
| 11 |
Supervised dimension reduction for ordinal predictors
|
Forzani, Liliana
|
|
2018
|
125 |
C |
p. 136-155
|
article
|
| 12 |
Unsupervised learning of mixture regression models for longitudinal data
|
Xu, Peirong
|
|
2018
|
125 |
C |
p. 44-56
|
article
|
Journal description