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Volumn 19, Issue 1, 2010, Pages 107-121

An evolutionary approach for imputing missing data in time series

Author keywords

Evolutionary optimization; Missing data imputation; Time series analysis

Indexed keywords

APPLICATION EXAMPLES; AUTOCORRELATION FUNCTIONS; ERROR FUNCTION; EVOLUTIONARY APPROACH; EVOLUTIONARY OPTIMIZATIONS; FITNESS FUNCTIONS; GENETIC STRUCTURE; METHODOLOGICAL ASPECTS; MISSING DATA; MISSING DATA IMPUTATION; MISSING OBSERVATIONS;

EID: 77951542208     PISSN: 02181266     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218126610006050     Document Type: Article
Times cited : (12)

References (28)
  • 1
    • 0002629270 scopus 로고
    • Maximum-likelihood from incomplete data via the em algorithm
    • A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum-likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39 (1977) 1-38.
    • (1977) J. R. Stat. Soc. , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 2
    • 3843106737 scopus 로고    scopus 로고
    • A multiple-imputation Metropolis version of the EM algorithm
    • DOI 10.1093/biomet/90.3.643
    • C. Gaetan and J.-F. Yao, A multiple-imputation metropolis version of the EM algorithm. Biometrika 90 (2003) 643-654. (Pubitemid 39047147)
    • (2003) Biometrika , vol.90 , Issue.3 , pp. 643-654
    • Gaetan, C.1    Yao, J.-F.2
  • 3
    • 50849098169 scopus 로고    scopus 로고
    • An improved estimator to analyse missing data
    • S. González, M. Rueda and A. Arcos, An improved estimator to analyse missing data. Stat. Paper. 49 (2008) 791-796.
    • (2008) Stat. Paper , vol.49 , pp. 791-796
    • González, S.1    Rueda, M.2    Arcos, A.3
  • 4
    • 34250704657 scopus 로고    scopus 로고
    • Semi-parametric optimization for missing data imputation
    • DOI 10.1007/s10489-006-0032-0
    • Y. Qin, S. Zhang, X. Zhu, J. Zhang and C. Zhang, Semi-parametric optimization for missing data imputation. Appl. Intell. 27 (2007) 79-88. (Pubitemid 46960845)
    • (2007) Applied Intelligence , vol.27 , Issue.1 , pp. 79-88
    • Qin, Y.1    Zhang, S.2    Zhu, X.3    Zhang, J.4    Zhang, C.5
  • 5
    • 60749123292 scopus 로고    scopus 로고
    • Missing data methods in longitudinal studies: A review
    • J. G. Ibrahim and G. Molenberghs, Missing data methods in longitudinal studies: A review. TEST 18 (2009) 1-43.
    • (2009) TEST , vol.18 , pp. 1-43
    • Ibrahim, J.G.1    Molenberghs, G.2
  • 9
    • 53049101431 scopus 로고    scopus 로고
    • Missing data imputation in time series by evolutionary algorithms
    • J. C. Figueroa, D. Kalenatic and C. A. Lopez, Missing data imputation in time series by evolutionary algorithms. Lect. Notes Comput. Sci. 5227 (2008) 275-283.
    • (2008) Lect. Notes Comput. Sci. , vol.5227 , pp. 275-283
    • Figueroa, J.C.1    Kalenatic, D.2    Lopez, C.A.3
  • 10
    • 33749072448 scopus 로고    scopus 로고
    • The use of genetic algorithms and neural networks to approximate missing data in database
    • DOI 10.1109/ICCCYB.2005.1511574, 1511574, ICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings
    • M. Abdella and T. Marwala, The use of genetic algorithms and neural networks to approximate missing data in database. IEEE 3rd Int. Conf. Computational Cybernetics, 2005, Vol. 3 (IEEE, 2005), pp. 207-212. (Pubitemid 44459048)
    • (2005) ICCC 2005 - IEEE 3rd International Conference on Computational Cybernetics - Proceedings , vol.2005 , pp. 207-212
    • Abdella, M.1    Marwala, T.2
  • 11
    • 85008025110 scopus 로고    scopus 로고
    • Using genetic algorithms to estimate confidence intervals for missing spatial data
    • N. H. W. Eklund, Using genetic algorithms to estimate confidence intervals for missing spatial data, IEEE Trans. Syst. Man Cybern. C Appl. Rev. 36 (2006) 519-523.
    • (2006) IEEE Trans. Syst. Man Cybern. C Appl. Rev , vol.36 , pp. 519-523
    • Eklund, N.H.W.1
  • 13
  • 16
    • 0004262735 scopus 로고
    • John Wiley and Sons, New York
    • P. Huber, Robust Statistics (John Wiley and Sons, New York, 1981).
    • (1981) Robust Statistics
    • Huber, P.1
  • 25
    • 0002241694 scopus 로고
    • The sem algorithm: A probabilistic teacher algorithm derived from the em algorithm for the mixture problem
    • G. Celeux and J. Diebolt, The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Comput. Stat. Q. 2 (1993) 73-82.
    • (1993) Comput. Stat. Q , vol.2 , pp. 73-82
    • Celeux, G.1    Diebolt, J.2
  • 26
    • 0035628553 scopus 로고    scopus 로고
    • Implementations of the monte-carlo em algorithm
    • L. A. Levine and G. Casella, Implementations of the Monte-Carlo EM algorithm, J. Comput. Graph. Stat. 10 (2000) 422-439.
    • (2000) J. Comput. Graph. Stat , vol.10 , pp. 422-439
    • Levine, L.A.1    Casella, G.2
  • 27
    • 0000795171 scopus 로고    scopus 로고
    • The stochastic em algorithm: Estimation and asymptotic results
    • S. F. Nielsen, The stochastic EM algorithm: Estimation and asymptotic results, Bernoulli 6 (2000) 457-489.
    • (2000) Bernoulli , vol.6 , pp. 457-489
    • Nielsen, S.F.1
  • 28
    • 0345376904 scopus 로고    scopus 로고
    • Reasoning about non-linear ar models using expectation maximization
    • M. Arnold. Reasoning about non-linear AR models using expectation maximization, J. Forecast. 22 (2003) 479-490.
    • (2003) J. Forecast , vol.22 , pp. 479-490
    • Arnold, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.