Smoothing with mixed model software

Journal of Statistical Software

Volumn 9, Issue , 2004, Pages 1-54

  Ngo, Long ^a   Wand, M P ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Additive mixed models; Additive models; Bivariate smoothing; Generalized additive models; Kriging; Scatterplot smoothing; Semiparametric mixed models; Semiparametric regression; Variance components; Varying coefficient models

Indexed keywords

EID: 4544224992     PISSN: 15487660     EISSN: None     Source Type: Journal    
DOI: 10.18637/jss.v009.i01     Document Type: Article

References (42)

1. Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. Oxford: Clarendon Press.
2. Breslow, N.E. and Clayton, D.G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9-25.
3. Brumback, B.A., Ruppert, D. and Wand, M.P. (1999). Comment on Shively, Kohn and Wood. Journal of the American Statistical Association, 94, 794-797.
4. Chaudhuri, P. and Marron, J.S. (1999). SiZer for exploration of structures in curves. Journal of the American Statistical Association, 94, 807-823.
5. Coull, B.A., Ruppert, D. and Wand, M.P. (2001). Simple incorporation of interactions into additive models. Biometrics, 57, 539-545.
6. Cressie, N. (1993). Statistics for Spatial Data. New York: John Wiley & Sons.
7. Diggle, P., Liang, K.-L. and Zeger, S. (1995). Analysis of Longitudinal Data. Oxford: Oxford University Press.
8. Draper, N.R. and Smith, H. (1998). Applied Regression Analysis (Third Edition). New York: John Wiley & Sons.
9. Eilers, P.H.C. and Marx, B.D. (1996). Flexible smoothing with B-splines and penalties (with discussion). Statistical Science, 11, 89-121.
10. French, J.L., Kammann, E.E. and Wand, M.P. (2001). Comment on Ke and Wang. Journal of the American Statistical Association, 96, 1285-1288.
11. Golub, G.H. and Van Loan, C.F. (1983). Matrix Computations (Third Edition). Baltimore: John Hopkins University Press.
12. Gray, R. J. (1992). Spline-based tests in survival analysis. Biometrics, 50, 640-652.
13. Green, P.J. (1987). Penalized likelihood for general semi-parametric regression models. International Statistical Review, 55, 245-259.
14. Kelly, C. and Rice, J. (1990). Monotone smoothing with application to dose-response curves and the assessment of synergism. Biometrics, 46, 1071-1085.
15. Härdle, W., Liang, H. and Gao, J. (2000). Partially Linear Models. Heidelberg: Physica-Verlag.
16. Hastie, T.J. (1996). Pseudosplines. Journal of the Royal Statistical Society, Series B, 58, 379-396.
17. Hastie, T.J. and Tibshirani, R.J. (1990). Generalized Additive Models. London: Chapman and Hall.
18. Johnson, M.E., Moore, L.M. and Ylvisaker, D. (1990). Minimax and maximin distance designs. Journal of Statistical Planning and Inference, 26, 131-148.
19. Kammann, E.E. and Wand, M.P. (2003). Geoadditive models. Applied Statistics, 52, 1-18.
20. Littell, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D. (1996). SAS System for Mixed Models, Cary, NC: SAS Institute Inc., 1996. 633 pp.
21. McCulloch, C.E., and Searle, S.R. (2000). Generalized, Linear, and Mixed Models. New York: John Wiley & Sons.
22. Nychka, D. and Saltzman, N. (1998). Design of Air Quality Monitoring Networks. In Case Studies in Environmental Statistics Nychka (D. Nychka, Cox, L., Piegorsch, W. eds.), Lecture Notes in Statistics, Springer-Verlag, 51-76.
23. Nychka, D., Haaland, P., O'Connell, M., Ellner, S. (1998). FUNFITS, data analysis and statistical tools for estimating functions. In Case Studies in Environmental Statistics (D. Nychka, W.W. Piegorsch, L.H. Cox, eds.), New York: Springer-Verlag, 159-179.
24. Nychka, D.W. (2000). Spatial process estimates as smoothers. In Smoothing and Regression (M. Schimek, ed.). Heidelberg: Springer-Verlag.
25. O'Connell, M.A. and Wolfinger, R.D. (1997). Spatial regression models, response surfaces, and process optimization. Journal of Computational and Graphical Statistics, 6, 224-241.
26. O'Sullivan, F. (1986). A statistical perspective on ill-posed inverse problems (with discussion). Statistical Science, 1, 505-527.
27. O'Sullivan, F. (1988). Fast computation of fully automated log-density and log-hazard estimators. SIAM Journal on Scientific and Statistical Computing, 9, 363-379.
28. Parker, R.L. and Rice, J.A. (1985). Discussion of "Some aspects of the spline smoothing approach to nonparametric curve fitting" by B.W. Silverman. Journal of the Royal Statistical Society, Series B, 47, 40-42.
29. Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. New York: Springer.
30. Ratkowsky, D. A. (1983). Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker.
31. Robinson, G.K. (1991). That BLUP is a good thing: the estimation of random effects. Statistical Science, 6, 15-51.
32. Ruppert, D. (2002). Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics, in press.
33. Ruppert, D. and Carroll, R.J. (2000). Spatially-adaptive penalties for spline fitting. Australian and New Zealand Journal of Statistics, 42, 205-224.
34. Ruppert, D., Wand, M. P. and Carroll, R.J. (2003). Semiparametric Regression. New York: Cambridge University Press.
35. Speed, T. (1991). Comment on paper by Robinson. Statistical Science, 6, 42-44.
36. Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer.
37. Verbyla, A.P. (1994). Testing linearity in generalized linear models. Contributed Pap. 17th Int. Biometric Conf., Hamilton, Aug. 8th-12th, 177.
38. Wahba, G. (1978). Improper priors, spline smoothing and the problem of guarding against model errors in regression. Journal of the Royal Statistical Society, Series B, 40, 364-372.
39. Wand, M. P. (2003). Smoothing and mixed models. Computational Statistics, to appear.
40. Wolfinger, R. and O'Connell, M. (1993). Generalized linear mixed models: a pseudolikelihood approach. Journal of Statistical Computation and Simulation, 48, 233-243.
41. Young, S. G. and Bowman, A. W. (1995). Non-parametric analysis of covariance. Biometrics, 51, 920-931.
42. Zanobetti, A., Wand, M.P., Schwartz, J. and Ryan, L.M. (2000). Generalized additive distributed lag models. Biostatistics, 1, 279-292.