SEARCH

SEARCH BY CITATION

REFERENCES

  • Artega, R. and A. Ferrer, “Dealing with Missing Data in MSPCS: Several Methods, Different Interpretations, Some Examples,” J. Chemometr. 16, 408418 (2002).
  • Baum, L. E., T. Petrie, G. Soules and N. Weiss, “A Maximization Technique Occurring in the Statistical Analysis of Probilistic Function of Markov Chains,” Ann. Math. Statist. 41, 164171 (1970).
  • Bjorck, A. and G. Golub, “Numerical Methods for Computing Angles between Linear Subspaces,” Math. Comput. 27, 579594 (1973).
  • Chen, J. and K. C. Liu, “On-Line Batch Process Monitoring Using Dynamic PCA and Dynamic PLS Models,” Chem. Eng. Sci. 57, 6375 (2002).
  • Damien, P., “Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables,” J.R. Statist. Soc. Ser. B(Methodol.) 61, 331344 (1999).
  • Dempster, A. P., N. M. Laird and D. B. Rubin, “Maximum Likelihood from Incomplete Data via EM Algorithm,” J. R. Statist. Soc. Ser. B (Methodol.) 39(1), 138 (1977).
  • Edwards, R. G. and A. D. Sokal, “Generalization of Fortuin-Kasteleyn-Swendsen-Wang Representation and Monte Carlo Algorithm,” Phys. Rev. Lett. 38, 20092012 (1988).
  • Fisher, R. A., “Theory of Statistical Estimation,” Proc. Camb. Phil. Soc. 22, 700725 (1925).
  • Gelman, A., J. B. Carlin, H. S. Stern and D. B. Rubin, “Bayesian Data Analysis,” 2nd ed., Chapman and Hall/CRC, Boca Raton, FL (2004).
  • Gollmer, K. and C. Postens, Detection of Distorted Pattern Using Dynamic Time Warping Algorithm and Application for Supervision of Bioprocess. “IFAC Workshop on On-Line Fault Detection and Supervision in Chemical Process Industries,” Newcastle (1995).
  • Grung, B. and R. Manne, “Missing Values in Principal Component Analysis,” Chemometr. Intell. Lab. Syst. 42, 125139 (1998).
  • Hartley, H. O., “Maximum Likelihood Estimation from Incomplete Data,” Biometrices 14(2), 174194 (1958).
  • Higdon, D. M., “Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications,” J. Am. Statist. Assoc. 93, 585595 (1998).
  • Imtiaz, S. A., M. A. A. S. Choudhury and S. L. Shah, “Building Multivarite Model from Compressed Data,” Ind. Eng. Chem. Res. 46, 481492 (2007).
  • Jennrich, R. I. and P. F. Sampson, “Rotation for Simple Loadings,” Psychometrika 31(3), 313323 (1966).
  • Knyazev, A. W. and M. E. Argentati, “Principal Angles between Subspaces in an a-based Scalar Product: Algorithms and Perturbation Estimates Data,” SIAM J. Sci. Compuat. 23(6), 20082040 (2002).
  • Kourti, T. P., J. Lee and J. F. MacGregor, “Experience with Industrial Applications of Projection Methods for Multivariate Statistical Process Control,” Comput. Chem. Eng. 20, (Suppl. A): S745S750 (1996).
  • Krzanowski, W. J., “Between-Groups Comparison of Principal Components,” J. Am. Statist. Assoc. 74(367), 703707 (1979).
  • Laird, N. M. and J. H. Ware, “Random-Effects Models for Longitudinal Data,” Biometrics 38, 963974 (1982).
  • Lakshminarayanan, S. R., R. D. Gudi, S. L. Shah and K. Nandakumar, Monitoring Batch Processes Using Multivariate Statistical Tools: Extensions and Practical Issues. “IFAC Triennial World Congress,” San Francisco (1996).
  • Li, K. H., “Hypothesis testing in multiple imputation with emphasis on mixed-up frequencies in contingency tables,” Unpublished PhD dissertation, University of Chicago, Statistics Department (1985).
  • Little, R. J. A. and D. B. Rubin, “Statistical Analysis with Missing Data,” Vol. 2, John Wiley and Sons, Inc., Hoboken, NJ (2002).
  • Liu, C. and D. B. Rubin, “The ECME Algorithm: A Simple Extension of EM and ECM with Faster Monotone Convergence,” Biometrika 81(4), 633648 (1994).
  • Luyben, W. L., “Process Modeling, Simulation and Control for Chemical Engineers,” McGraw-Hill, New York (1990).
  • Marjanovic, O., B. Lennox, D. Sandoz, K. Smith and M. Crofts, “Real-Time Monitoring of an Industrial Batch Process,” Comput. Chem. Eng. 30, 14761481 (2006).
  • McKendrick, A. G., “Application of Mathematics to Medical Problems,” 44, 98130 (1926).
  • Meng, X. L. and D. B. Rubin, “Maximum Likelihood Estimation via the ECM Algorithm: A General Framework,” Biometrika 80, 267278 (1993).
  • Meng X. L. and D. A. van Dyk, “The EM Algorithm—An Old Folk Song Sung to a Fast New Tune (with Discussion),” J. R. Statist. Soc. B 59, 511567 (1997).
  • Mira, A. and L. Tierney, “On the Use of Auxiliary Variables in Markov Chain Monte Carlo Sampling,” Technical Report 59 (1997).
  • Myers, C., L. R. Rabiner and A. E. Rosenberg, “Performance Tradeoffs in Dynamic Time Warping Algorithms for Isolated Word Recognition,” IEEE Trans. Acuost. Speech Signal Process. ASSP-28(6): 623. (1980).
  • Narasimhan, S. and S. L. Shah, “Model Identification and Error Covariance Matrix Estimation from Noisy Data Using PCA,” Presentation, ADCHEM, Hong Kong (2004).
  • Neal, R. M., “Markov Chain Monte Carlo Methods Based on 'Slicing' the Density Function,” Technical Report No. 9722, Department of Statistics, University of Toronto (1997).
  • Nelson, P. R. C., P. A. Taylor and J. F. MacGregor, “Missing Data Methods in PCA and PLS: Score Calculations with Incomplete Observations,” Chemometr. Intell. Lab. Syst. 35, 45465 (1996).
  • Nielsen, S. F., “Proper and Improper Multiple Imputation,” Int. Statist. Rev. 71(3), 593627 (2003).
  • Nomikos, P. and J. F. MacGregor, “Monitoring Batch Processes Using Multiway Principal Component Analysis,” AIChE J. 40(8), 13611375 (1994).
  • Nomikos, P. and J. F. MacGregor, “Multi-Way Partial Least Squares in Monitoring Batch Processes,” Chemometr. Intell. Lab. Syst. 30, 97108 (1995).
  • Nounou, M. N., B. R. Bakshi, P. K. Goel and X. Shen, “Bayesian Principal Component Analysis,” J. Chemometr. 16, 576595 (2002).
  • Orchard, T. and M. A. Woodbury, “A Missing Information Principle: Theory and Applications,” Proc. 6th Berkeley Symposium on Math Statist. and Prob. 1, 697715 (1972).
  • O'Shaughnessy, D., “Speaker Recognition,” IEEE ASSP Mag. 3, 417 (1986).
  • Roberts, G. O. and J. S. Rosenthal, “Convergence of Slice Sampler Markov Chains,” Technical Report, Statistical Laboratory, Cambridge University (1997).
  • Rubin, D. B., “Inference and Missing Data,” Biometrika 63, 581592 (1976).
  • Rubin, D. B., “Formalizing Subjective Notions about the Effect of Nonrespondents in Sample Surveys,” J. Am. Statist. Assoc. 72, 538543 (1977).
  • Rubin, D. B., “Bayesian Inference for Causal Effects: The Role of Randomization,” Ann. Statist. 7, 3458 (1978).
  • Rubin, D. B., “Multiple Imputations for Nonresponse in Surveys,” John Wiley and Sons Inc., Hoboken, NJ (1987).
  • Rubin, D. B., “Discussion on Multiple Imputation,” Int. Statist. Rev. 71(3), 619625 (2003).
  • Schafer, J. L. and N. Schenker, “Missing Data: Our View of the State of the Art,” Psychol. Methods 7(2), 147177 (2002).
  • Silverman, H. F. and D. P. Morgan, “The Application of Dynamic Programming to Connected Speech Recognition,” IEEE ASSP Mag. 7, 625 (1990).
  • Sundberg, R., “An Iterative Method for Solution of the Likelihood Equations for Incomplete Data from Exponential Families,” Communs. Statist. Simuln. Computn. 5, 5564 (1976).
  • Swendsen, R. H. and J. S. Wang, “Nonuniversal Critical Dynamics in Monte Carlo Simulation,” Phys. Rev. Lett. 58, 8688 (1987).
  • Tanner, M. A. and W. H. Wong, “The Calculation of Posterior Distribution by Data Augmentation,” J. Am. Statist. Assoc. 82(398), 528540 (1990).
  • Thornhill, N. F., M. A. A. S. Choudhury and S. L. Shah, “The Impact of Compression on Data-Driven Process Analysis,” J. Process Control 14, 389398 (2004).
  • Troyanskaya, O., M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein and R. B. Altman, “Missing Value Estimation Methods for DNA Microarrays,” Bioinfomatics 17(6), 520525 (2001).
  • VanDyk, D. A. and X.-L. Meng, “The Art of Data Augmentation,” J. Comput. Graph. Statist. 10(1), 150 (2001).
  • van Sprang, E. N. M., H. J. Ramaker, J. A. Westerhuis, S. P. Gurden and A. K. Smilde*Critical Evaluation of Approaches for On-Line Batch Process Monitoring,” Chem. Eng. Sci. 57, 39793991 (2002).
  • Walczak, B. and D. L. Massart, “Dealing with Missing Data,” Chemometr. Intell. Lab. Syst. 58, 1527 (2001).
  • Westerhuis, J. A., T. Kourti and J. F. MacGregor, “Comparing Alternative Approaches for Multivariate Statistical Analysis of Batch Process Data,” J. Chemometr. 13, 397413 (1999).
  • Zhang, P., “Multiple Imputation: Theory and Method,” Int. Statist. Rev. 71(3), 581592. (2003).