A Newton procedure for conditionally linear mixed-effects models

Behavior Research Methods

Volumn 39, Issue 4, 2007, Pages 695-708

  Blozis, Shelley A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 37349122502     PISSN: 1554351X     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03192960     Document Type: Article

References (47)

1. AMEMIYA, Y. ( 1993). Instrumental variable estimation for nonlinear factor analysis. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate analysis: Future directions 2 (pp. 113-129). Amsterdam: Elsevier.
2. BLOZIS, S. A. (2004). Structured latent curve models for the study of change in multivariate repeated measures. Psychological Methods, 9, 334-353.
3. BLOZIS, S. A., & CUDECK, R. (1999). Conditionally linear mixed-effects models with latent variable covariates. Journal of Educational & Behavioral Statistics, 24, 245-270.
4. BROWNE, M. W. (1993). Structured latent curve models. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate analysis: Future directions 2 (pp. 171-197). Amsterdam: Elsevier.
5. BROWNE, M. W., & DU TOIT, S. H. C. (1991). Models for learning data. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change (pp. 47-68). Washington: American Psychological Association.
6. COLLINS, L. M., & GRAHAM, J. W. (2002). The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug & Alcohol Dependence, 68, S85-S96.
7. CURRAN, P. J. (1997, April). The bridging of quantitative methodology and applied developmental research. In P. J. Curran (Chair), Comparing three modern approaches to longitudinal data analysis: An examination of a single developmental sample. Symposium conducted at the 1997 meeting of the Society for Research on Child Development, Washington, DC.
8. DAVIDIAN, M., & GILTINAN, D. M. (1995). Nonlinear models for repeated measurement data. New York: Chapman & Hall.
9. DENNIS, J. E., & SCHNABEL, R. B. (1986). Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs, NJ: Prentice Hall.
10. ETEZADI-AMOLI, J., & MCDONALD, R. P. (1983). A second generation nonlinear factor analysis. Psychometrika, 48, 315-342.
11. FINKBEINER, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44, 409-420.
12. FRANCIS, D. J., SHAYWTTZ, S. E., STUBBING, K. K., SHAYWITZ, B. A., & FLETCHER, J. M. (1996). Developmental lag versus deficit models of reading disability: A longitudinal, individual growth curves analysis. Journal of Educational Psychology, 88, 3-17.
13. GOLDSTEIN, H., RASBASH, J., PLEWIS, I., DRAPER, D., BROWNE, W., YANG, M., ET AL. (1998). A user's guide to MLwiN (Tech. Rep.). London: University of London, Institute of Education.
14. HEDEKER, D., & GIBBONS, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78.
15. HOX, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.
16. JAMSHIDIAN, M., & JENNRICH, R. I. (1997). Acceleration of the EM algorithm using quasi-Newton methods. Journal of the Royal Statistical Society: Series B, 59, 569-587.
17. JENNRICH, R. I., & SCHLUCHTER, M. D. (1986). Unbalanced repeated measures models with structured covariance matrices. Biometrics, 42, 805-820.
18. JÖRESKOG, K. G., SÖRBOM, D., DU TOIT, S., & DU TOIT, M. (2001). LISREL 8: New statistical features. Lincolnwood, IL: Scientific Software International.
19. KREFT, I., & DE LEEUW, J. (1998). Introducing multilevel modeling. Thousand Oaks, CA: Sage.
20. LAIRD, N. M. (1988). Missing data in longitudinal studies. Statistics in Medicine, 7, 305-315.
21. LAWLEY, D. N., & MAXWELL, A. E. (1971 ). Factor analysis as a statistical method. New York: Elsevier.
22. LINDSTROM, M. J., & BATES, D. M. (1988). Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association, 83, 1014-1022.
23. LITTELL, R. C., MILLIKEN, G. A., STROUP, W. W., WOLFINGER, R. D., & SCHABENBERGER, O. (2006). SAS for mixed models (2nd ed.). Cary, NC: SAS Institute.
24. LITTLE, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112-1121.
25. LITTLE, R. J. A., & RUBIN, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.
26. LONGFORD, N. [T.] (1990). VARCL: Software for variance component analysis of data with nested random effects (maximum likelihood). Princeton, NJ: Educational Testing Service.
27. LONGFORD, N. T. (1993). Random coefficient models. Oxford: Oxford University Press.
28. MACCALLUM, R. C., KIM, C., MALARKEY, W. B., & KIECOLT-GLASER, J. K. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32, 215-253.
29. MATHSOFT, INC. (1997). S-PLUS user's guide. Seattle: Author.
30. MCDONALD, R. P. (1962). A general approach to nonlinear factor analysis. Psychometrika, 27, 397-415.
31. MCDONALD, R. P. (1965). Difficulty factors and nonlinear factor analysis. British Journal of Mathematical & Statistical Psychology, 18, 11-23.
32. MCDONALD, R. P. (1967a). Factor interaction in nonlinear factor analysis. British Journal of Mathematical & Statistical Psychology, 20, 205-215.
33. MCDONALD, R. P. (1967b). Numerical methods for polynomial models in nonlinear factor analysis. Psychometrika, 32, 77-112.
34. MEREDITH, W., & TISAK, J. ( 1984). Juckerizing curves. Paper presented at the annual meeting of the Psychometric Society, Santa Barbara, CA.
35. MEREDITH, W., & TISAK, J. ( 1990). Latent curve analysis. Psychometrika, 55, 107-122.
36. MUTHÉN, L. K., & MUTHÉN, B. O. (1998-2004). Mplus user's guide (3rd ed.). Los Angeles: Authors.
37. NEALE, M. C., BOKER, S. M., XIE, G., & MAES, H. H. (2003). Mx: Statistical modeling (6th ed.). Richmond, VA: Virginia Commonwealth University, Department of Psychiatry.
38. RAO, C. R. (1973). Linear statistical inference and its applications (2nd ed.). New York: Wiley. RAUDENBUSH, S. W., & BRYK, A. S. (2002). Hierarchical linear models. Thousand Oaks, CA: Sage.
39. RAUDENBUSH, S. W., BRYK, A. S., CHEONG, Y. R, & CONGDON, R. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.
40. RUBIN, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
41. SHAH, B. V., BARNWELL, B. G., & BIELER, G. S. (1997). SUDAAN user's manual (Version 7.5). Research Triangle Park, NC: Research Triangle Institute.
42. SINGER, J. D., & WILLETT, J. B. (2003). Applied longitudinal data analysis. Oxford: Oxford University Press.
43. SNIJDERS, T., & BOSKER, R. (1999). Multilevel analysis. London: Sage.
44. SPSS FOR WINDOWS, REL. II.O.I. (2001). Chicago: SPSS.
45. STATA CORPORATION (1997). STATA reference manual. College Station, TX: Stata Press.
46. WOLFINGER, R. (1999). Fitting nonlinear mixed models with the new NLMIXED procedure. In Proceedings of the twenty-fourth annual SAS Users Group International Conference (paper 287), 1-10. (www2.sas.com/proceedings/ sugi24/Stats/p287-24.pdf).
47. YALCIN, I., & AMEMIYA, Y. (2001 ). Nonlinear factor analysis as a statistical method. Statistical Science, 16, 275-294.