nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
A scale space approach for exploring structure in spherical data
|
Vuollo, Ville |
|
2018 |
125 |
C |
p. 57-69 |
artikel |
2 |
Continuum directions for supervised dimension reduction
|
Jung, Sungkyu |
|
2018 |
125 |
C |
p. 27-43 |
artikel |
3 |
Editorial Board
|
|
|
2018 |
125 |
C |
p. ii-iv |
artikel |
4 |
Identification of local sparsity and variable selection for varying coefficient additive hazards models
|
Qu, Lianqiang |
|
2018 |
125 |
C |
p. 119-135 |
artikel |
5 |
Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics
|
Alam, Md. Ashad |
|
2018 |
125 |
C |
p. 70-85 |
artikel |
6 |
Joint regression analysis of mixed-type outcome data via efficient scores
|
Marchese, Scott |
|
2018 |
125 |
C |
p. 156-170 |
artikel |
7 |
On the sample mean after a group sequential trial
|
Berckmoes, Ben |
|
2018 |
125 |
C |
p. 104-118 |
artikel |
8 |
Point process models for novelty detection on spatial point patterns and their extremes
|
Luca, Stijn E. |
|
2018 |
125 |
C |
p. 86-103 |
artikel |
9 |
Robust template estimation for functional data with phase variability using band depth
|
Cleveland, Jason |
|
2018 |
125 |
C |
p. 10-26 |
artikel |
10 |
Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data
|
Wang, Shuying |
|
2018 |
125 |
C |
p. 1-9 |
artikel |
11 |
Supervised dimension reduction for ordinal predictors
|
Forzani, Liliana |
|
2018 |
125 |
C |
p. 136-155 |
artikel |
12 |
Unsupervised learning of mixture regression models for longitudinal data
|
Xu, Peirong |
|
2018 |
125 |
C |
p. 44-56 |
artikel |