메뉴 건너뛰기




Volumn 71, Issue , 2014, Pages 1-2

The 2nd special issue on advances in mixture models

Author keywords

[No Author keywords available]

Indexed keywords


EID: 84889098972     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2013.10.010     Document Type: Editorial
Times cited : (7)

References (27)
  • 2
    • 84889099616 scopus 로고    scopus 로고
    • Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data
    • S. Bacci, and F. Bartolucci Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data Computational Statistics and Data Analysis 71 2014 262 272
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 262-272
    • Bacci, S.1    Bartolucci, F.2
  • 3
    • 0027453616 scopus 로고
    • Model-based Gaussian and non-Gaussian clustering
    • J.D. Banfield, and A.E. Raftery Model-based Gaussian and non-Gaussian clustering Biometrics 49 1993 803 821 (Pubitemid 23298358)
    • (1993) Biometrics , vol.49 , Issue.3 , pp. 803-821
    • Banfield, J.D.1    Raftery, A.E.2
  • 6
    • 0029305528 scopus 로고
    • Gaussian parsimonious clustering models
    • G. Celeux, and G. Govaert Gaussian parsimonious clustering models Pattern Recognition 28 1995 781 793
    • (1995) Pattern Recognition , vol.28 , pp. 781-793
    • Celeux, G.1    Govaert, G.2
  • 7
    • 84888857815 scopus 로고    scopus 로고
    • Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data
    • N. Coffey, J. Hinde, and E. Holian Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data Computational Statistics and Data Analysis 71 2014 14 29
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 14-29
    • Coffey, N.1    Hinde, J.2    Holian, E.3
  • 8
    • 84889097290 scopus 로고    scopus 로고
    • A multivariate linear regression analysis using finite mixtures of t distributions
    • G. Galimberti, and G. Soffritti A multivariate linear regression analysis using finite mixtures of t distributions Computational Statistics and Data Analysis 71 2014 138 150
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 138-150
    • Galimberti, G.1    Soffritti, G.2
  • 11
    • 84888863625 scopus 로고    scopus 로고
    • A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance
    • S. Jaspers, M. Aerts, G. Verbeke, and P.-A. Beloeil A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance Computational Statistics and Data Analysis 71 2014 30 42
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 30-42
    • Jaspers, S.1    Aerts, M.2    Verbeke, G.3    Beloeil, P.-A.4
  • 12
    • 84889089234 scopus 로고    scopus 로고
    • Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes
    • M. Lesperance, R. Saab, and J. Neuhaus Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes Computational Statistics and Data Analysis 71 2014 211 219
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 211-219
    • Lesperance, M.1    Saab, R.2    Neuhaus, J.3
  • 14
    • 84889102495 scopus 로고    scopus 로고
    • Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
    • T.-I. Lin Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition Computational Statistics and Data Analysis 71 2014 183 195
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 183-195
    • Lin, T.-I.1
  • 15
    • 79956130569 scopus 로고    scopus 로고
    • Bias from misspecification of the component variances in a normal mixture
    • Y. Lo Bias from misspecification of the component variances in a normal mixture Computational Statistics and Data Analysis 55 2011 2739 2747
    • (2011) Computational Statistics and Data Analysis , vol.55 , pp. 2739-2747
    • Lo, Y.1
  • 16
    • 84889097028 scopus 로고    scopus 로고
    • Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application
    • Z. Lu, and Z. Zhang Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application Computational Statistics and Data Analysis 71 2014 220 240
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 220-240
    • Lu, Z.1    Zhang, Z.2
  • 17
    • 34548307049 scopus 로고    scopus 로고
    • Robust fitting of mixtures using the trimmed likelihood estimator
    • DOI 10.1016/j.csda.2006.12.024, PII S0167947306005019
    • N. Neykov, P. Filzmoser, R. Dimova, and P. Neytchev Robust fitting of mixtures using the trimmed likelihood estimator Computational Statistics and Data Analysis 52 2007 299 308 (Pubitemid 47336983)
    • (2007) Computational Statistics and Data Analysis , vol.52 , Issue.1 , pp. 299-308
    • Neykov, N.1    Filzmoser, P.2    Dimova, R.3    Neytchev, P.4
  • 20
    • 84889103165 scopus 로고    scopus 로고
    • Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection
    • S. Pledger, and R. Arnold Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection Computational Statistics and Data Analysis 71 2014 241 261
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 241-261
    • Pledger, S.1    Arnold, R.2
  • 21
    • 84889080059 scopus 로고    scopus 로고
    • A combined likelihood ratio/information ratio bootstrap technique for estimating the number of components in finite mixtures
    • A. Polymenis A combined likelihood ratio/information ratio bootstrap technique for estimating the number of components in finite mixtures Computational Statistics and Data Analysis 71 2014 107 115
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 107-115
    • Polymenis, A.1
  • 24
  • 26
    • 84889087967 scopus 로고    scopus 로고
    • Parsimonious skew mixture models for model-based clustering and classification
    • I. Vrbik, and P.D. McNicholas Parsimonious skew mixture models for model-based clustering and classification Computational Statistics and Data Analysis 71 2014 196 210
    • (2014) Computational Statistics and Data Analysis , vol.71 , pp. 196-210
    • Vrbik, I.1    McNicholas, P.D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.