BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

223 related articles for article (PubMed ID: 26283988)

  • 21. Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data.
    de Haan-Rietdijk S; Voelkle MC; Keijsers L; Hamaker EL
    Front Psychol; 2017; 8():1849. PubMed ID: 29104554
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.
    Monserud RA; Marshall JD
    Tree Physiol; 2001 Sep; 21(15):1087-102. PubMed ID: 11581016
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Autoregressive single-step test-day model for genomic evaluations of Portuguese Holstein cattle.
    Silva AA; Silva DA; Silva FF; Costa CN; Lopes PS; Caetano AR; Thompson G; Carvalheira J
    J Dairy Sci; 2019 Jul; 102(7):6330-6339. PubMed ID: 31056320
    [TBL] [Abstract][Full Text] [Related]  

  • 24. How to compare cross-lagged associations in a multilevel autoregressive model.
    Schuurman NK; Ferrer E; de Boer-Sonnenschein M; Hamaker EL
    Psychol Methods; 2016 Jun; 21(2):206-21. PubMed ID: 27045851
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A double bootstrap method to analyze linear models with autoregressive error terms.
    McKnight SD; McKean JW; Huitema BE
    Psychol Methods; 2000 Mar; 5(1):87-101. PubMed ID: 10937324
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Person-specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels.
    Liu S
    Br J Math Stat Psychol; 2017 Nov; 70(3):480-498. PubMed ID: 28225554
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model.
    Zhuang Z; Zhai X; Ben X; Wang B; Yuan D
    PeerJ Comput Sci; 2021; 7():e482. PubMed ID: 33977132
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry.
    Lu S; Ju KH; Chon KH
    IEEE Trans Biomed Eng; 2001 Oct; 48(10):1116-24. PubMed ID: 11585035
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Changing dynamics: Time-varying autoregressive models using generalized additive modeling.
    Bringmann LF; Hamaker EL; Vigo DE; Aubert A; Borsboom D; Tuerlinckx F
    Psychol Methods; 2017 Sep; 22(3):409-425. PubMed ID: 27668421
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Uncertainty analysis in matched-field geoacoustic inversions.
    Huang CF; Gerstoft P; Hodgkiss WS
    J Acoust Soc Am; 2006 Jan; 119(1):197-207. PubMed ID: 16454276
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.
    De Haan-Rietdijk S; Gottman JM; Bergeman CS; Hamaker EL
    Psychometrika; 2016 Mar; 81(1):217-41. PubMed ID: 25091047
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Using the time-varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions.
    Casini E; Richetin J; Preti E; Bringmann LF
    J Pers; 2020 Aug; 88(4):806-821. PubMed ID: 31784985
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Estimation of the parameters of symmetric stable ARMA and ARMA-GARCH models.
    Sathe AM; Upadhye NS
    J Appl Stat; 2022; 49(11):2964-2980. PubMed ID: 35909668
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada.
    Petukhova T; Ojkic D; McEwen B; Deardon R; Poljak Z
    PLoS One; 2018; 13(6):e0198313. PubMed ID: 29856881
    [TBL] [Abstract][Full Text] [Related]  

  • 35. SEM Based CARMA Time Series Modeling for Arbitrary N.
    Oud JHL; Voelkle MC; Driver CC
    Multivariate Behav Res; 2018; 53(1):36-56. PubMed ID: 29111788
    [TBL] [Abstract][Full Text] [Related]  

  • 36. An improved portmanteau test for autocorrelated errors in interrupted time-series regression models.
    Huitema BE; McKean JW
    Behav Res Methods; 2007 Aug; 39(3):343-9. PubMed ID: 17958144
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Choosing between AR(1) and VAR(1) models in typical psychological applications.
    Dablander F; Ryan O; Haslbeck JMB
    PLoS One; 2020; 15(10):e0240730. PubMed ID: 33119716
    [TBL] [Abstract][Full Text] [Related]  

  • 38. VAR(1) based models do not always outpredict AR(1) models in typical psychological applications.
    Bulteel K; Mestdagh M; Tuerlinckx F; Ceulemans E
    Psychol Methods; 2018 Dec; 23(4):740-756. PubMed ID: 29745683
    [TBL] [Abstract][Full Text] [Related]  

  • 39. In praise of Prais-Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series.
    Bottomley C; Ooko M; Gasparrini A; Keogh RH
    Stat Med; 2023 Apr; 42(8):1277-1288. PubMed ID: 36722328
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Bayesian change-point modeling with segmented ARMA model.
    Sadia F; Boyd S; Keith JM
    PLoS One; 2018; 13(12):e0208927. PubMed ID: 30596668
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 12.