These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

142 related articles for article (PubMed ID: 31851318)

  • 1. Covariate Assisted Principal regression for covariance matrix outcomes.
    Zhao Y; Wang B; Mostofsky SH; Caffo BS; Luo X
    Biostatistics; 2021 Jul; 22(3):629-645. PubMed ID: 31851318
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Longitudinal regression of covariance matrix outcomes.
    Zhao Y; Caffo BS; Luo X
    Biostatistics; 2024 Apr; 25(2):385-401. PubMed ID: 36451549
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Principal regression for high dimensional covariance matrices.
    Zhao Y; Caffo B; Luo X;
    Electron J Stat; 2021; 15(2):4192-4235. PubMed ID: 35782590
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Bayesian estimation of covariate assisted principal regression for brain functional connectivity.
    Park HG
    Biostatistics; 2024 Jul; ():. PubMed ID: 38981041
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Semiparametric partial common principal component analysis for covariance matrices.
    Wang B; Luo X; Zhao Y; Caffo B
    Biometrics; 2021 Dec; 77(4):1175-1186. PubMed ID: 32935852
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A whole-brain modeling approach to identify individual and group variations in functional connectivity.
    Zhao Y; Caffo BS; Wang B; Li CR; Luo X
    Brain Behav; 2021 Jan; 11(1):e01942. PubMed ID: 33210469
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates.
    Jacob BG; Griffith DA; Muturi EJ; Caamano EX; Githure JI; Novak RJ
    Malar J; 2009 Sep; 8():216. PubMed ID: 19772590
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A random covariance model for bi-level graphical modeling with application to resting-state fMRI data.
    Zhang L; DiLernia A; Quevedo K; Camchong J; Lim K; Pan W
    Biometrics; 2021 Dec; 77(4):1385-1396. PubMed ID: 32865813
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Functional Brain Network Classification With Compact Representation of SICE Matrices.
    Zhang J; Zhou L; Wang L; Li W
    IEEE Trans Biomed Eng; 2015 Jun; 62(6):1623-34. PubMed ID: 25667346
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Linear mixed-effects modeling approach to FMRI group analysis.
    Chen G; Saad ZS; Britton JC; Pine DS; Cox RW
    Neuroimage; 2013 Jun; 73():176-90. PubMed ID: 23376789
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Semiparametric estimation of covariance matrices for longitudinal data.
    Fan J; Wu Y
    J Am Stat Assoc; 2008 Dec; 103(484):1520-1533. PubMed ID: 19180247
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Linear mixed function-on-function regression models.
    Wang W
    Biometrics; 2014 Dec; 70(4):794-801. PubMed ID: 24975922
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure.
    Pavlović DM; Guillaume BRL; Towlson EK; Kuek NMY; Afyouni S; Vértes PE; Yeo BTT; Bullmore ET; Nichols TE
    Neuroimage; 2020 Oct; 220():116611. PubMed ID: 32058004
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Model-based principal components of covariance matrices.
    Boik RJ; Panishkan K; Hyde SK
    Br J Math Stat Psychol; 2010 Feb; 63(Pt 1):113-37. PubMed ID: 19534846
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Generalized linear models with coarsened covariates: a practical Bayesian approach.
    Johnson TR; Wiest MM
    Psychol Methods; 2014 Jun; 19(2):281-99. PubMed ID: 24364382
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A simulation study to compare robust tests for linear mixed-effects meta-regression.
    Welz T; Pauly M
    Res Synth Methods; 2020 May; 11(3):331-342. PubMed ID: 31930705
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A three domain covariance framework for EEG/MEG data.
    Roś BP; Bijma F; de Gunst MC; de Munck JC
    Neuroimage; 2015 Oct; 119():305-15. PubMed ID: 26072253
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Common reducing subspace model and network alternation analysis.
    Wang W; Zhang X; Li L
    Biometrics; 2019 Dec; 75(4):1109-1120. PubMed ID: 31140579
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data.
    Kohli P; Garcia TP; Pourahmadi M
    J Multivar Anal; 2016 Mar; 145():87-100. PubMed ID: 38993393
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.
    Scheffler AW; Dickinson A; DiStefano C; Jeste S; Şentürk D
    Stat Interface; 2022; 15(2):209-223. PubMed ID: 35664510
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.