Generalized semiparametrically structured ordinal models

Biometrics

Volumn 59, Issue 2, 2003, Pages 263-273

  Tutz, Gerhard ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

AIC criterion; Category specific effects; Ordinal regression; Proportional odds model; Semiparametric model; Varying coefficients

Indexed keywords

AIC CRITERION; CATEGORY SPECIFICS; CATEGORY-SPECIFIC EFFECT; FUNCTIONAL FORMS; ORDINAL MODELS; ORDINAL REGRESSION; PROPORTIONAL ODDS MODEL; SEMI-PARAMETRIC MODELING; SPECIFIC EFFECTS; VARYING COEFFICIENTS;

ANTIINFLAMMATORY AGENT; PLACEBO;

BIOMETRY; STATISTICAL ANALYSIS;

ARTICLE; BIOSTATISTICS; CIGARETTE SMOKING; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DIABETES MELLITUS; DIABETIC RETINOPATHY; DIASTOLIC BLOOD PRESSURE; DISEASE DURATION; HUMAN; KNEE INJURY; MAJOR CLINICAL STUDY; STATISTICAL MODEL; STATISTICAL PARAMETERS; TREATMENT OUTCOME;

EID: 0037869562     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/1541-0420.00033     Document Type: Article

References (34)

1. Agresti, A. (1984). Analysis of Ordinal Categorical Data. New York: Wiley.
2. Agresti, A. (1999). Modelling ordered categorical data: Recent advances and future challenges. Statistics in Medicine 18, 2191-2207.
3. Albert, J. and Chib, S. (2001). Sequential ordinal modelling with applications to survival data. Biometrics 57, 829-836.
4. Armstrong, B. and Sloan, M. (1989). Ordinal regression models for epidemiologic data. American Journal of Epidemiology 129, 191-204.
5. Barnhart, H. and Sampson, A. (1994). Overviews of multinomial models for ordinal data. Communications in Statistics - Theory and Methods 23(12), 3395-3416.
6. Bender, R. and Grouven, U. (1998). Using binary logistic regression models for ordinal data with non-proportional odds. Journal of Clinical Epidemiology 51, 809-816.
7. Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics 46, 1171-1178.
8. Cox, C. (1988). Multinomial regression models based on continuation ratios. Statistics in Medicine 7, 433-441.
9. Eilers, P. H. C. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science 11, 89-121.
10. Eilers, P. H. C. and Marx, B. D. (2003). Generalized linear additive smooth structures. Journal of Computational and Graphical Statistics, to appear.
11. Fahrmeir, L. and Tutz, G. (2001). Multivariate Statistical Modelling based on Generalized Linear Models, 2nd edition. New York: Springer.
12. Goodman, L. A. (1983). The analysis of dependence in cross-classification having ordered categories, using log-linear models for frequencies and log-linear models for odds. Biometrika 39, 149-160.
13. Green, D. J. and Silverman, B. W. (1994). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. London: Chapman and Hall.
14. Greenland, S. (1994). Alternative models for ordinal logistic regression. Statistics in Medicine 13, 1665-1677.
15. Hastie, T. and Tibshirani, R. (1987). Non-parametric logistic and proportional odds regression. Applied Statistics 36, 260-276.
16. Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models. London: Chapman and Hall.
17. Hastie, T. and Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, Series B 55, 757-796.
18. Hurvich, C. M., Simonoff, J. S., and Tsai, C. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society, Series B 60, 271-293.
19. Kauermann, G. and Tutz, G. (2000). Local likelihood estimation in varying coefficient models including additive bias correction. Journal of Nonparametric Statistics 12, 343-371.
20. Läärä, E. and Matthews, J. N. (1985). The equivalence of two models for ordinal data. Biometrika 72, 206-207.
21. McCullagh, P. (1980). Regression model for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B 42, 109-127.
22. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edition. New York: Chapman and Hall.
23. Parise, H., Wand, M., Ruppert, D., and Ryan, L. (2001). In-corporation of historical controls using semiparametric mixed models. Applied Statistics 50, 31-42.
24. Ruppert, D. (2003). Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics 11, 735-757.
25. Ruppert, D. and Carroll, R. J. (1999). Spatially-adaptive penalties for spline fitting. Australian Journal of Statistics 42, 205-223.
26. Severini, T. A. and Staniswalis, J. G. (1994). Quasi-likelihood estimation in semiparametric models. Journal of the American Statistical Association 89, 501-511.
27. Simon, G. (1974). Alternative analyses for singly-ordered contingency table. Journal of the American Statistical Association 69, 971-976.
28. Speckman, P. (1988). Kernel smoothing in partial linear models. Journal of the Royal Statistical Society, Series B 50, 413-436.
29. Tutz, G. (1991). Sequential models in ordinal regression. Computational Statistics and Data Analysis 11, 275-295.
30. Tutz, G. (2000). Die Analyse kategorialer Daten - Eine anwendungsorientierte Einführung in Logit-Modellierung und kategoriale Regression. München, Germany: Oldenbourg Verlag.
31. Tutz, G. and Scholz, T. (2003). Semiparametric modelling of categerical data. Journal of Statistical Computation and Simulation, to appear.
32. Wand, M. (2003). Smoothing and mixed models. Computational Statistics. in press.
33. Wilkinson, G. N. and Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics 22, 392-399.
34. Yee, T. W. and Wild, C. J. (1996). Vector generalized additive models. Journal of the Royal Statistical Society, Series B 58, 481-493.