Book

(3) Geerdens, C., Claeskens, G. & Janssen, P. "Goodness-of-fit tests for the frailty distribution in proportional hazards models with shared frailty".

(4) Behl, P., Claeskens, G. & Dette, H. "Focused model selection in quantile regression".

Publications

Please click on a number to view the corresponding paper.

(49) Slaets, L., Claeskens, G. & Hubert, M.  (2012). "Phase and amplitude-based clustering for functional data" Example R-code. Computational Statistics and Data Analysis, 56, 2360-2374.

(48) Claeskens, G.  (2012). "Focused estimation and model averaging with penalization methods, an overview". To appear in Statistica Neerlandica. The definitive version is going to be available at www.blackwell-synergy.com.

(47) Tharmaratnam, K. & Claeskens, G. (2012). "A comparison of robust versions of the AIC based on M, S and MM estimators". Statistics: A Journal of Theoretical and Applied Statistics, to appear. R-code

(46) Vansteelandt, S., Bekaert, M. & Claeskens, G. (2012). "On model selection and model misspecification in causal inference". Statistical Methods in Medical 
Research, 21(1), 7-30.

(45) Claeskens, G., Ding, H. & Jansen, M. (2011). "Lack-of-fit tests in linear mixed models with application to wavelet tests". Journal of Nonparametric Statistics, 11(5), 409-427.

(44)  Ding, H., Claeskens G. & Jansen, M. (2011).  "Variable selection in partially linear wavelet models", Statistical Modelling, 11(5), 409-427.

(43)  Consentino, F. and Claeskens, G. (2011). "Missing covariates in logistic regression, estimation and
        distribution selection". Statistical Modelling, 11, 69-93.  R code

(42)  Claeskens, G., Silverman, B. & Slaets, L (2010). "Time warping achieved by a Bayesian prior-posterior transfer fitting strategy". Journal of the Royal Statistical Society - Series B, 72(5), 673-694.

R library MRwarp (This software comes as it is. No guarantee whatsoever is given. The authors cannot be held responsible for any misunderstanding, incorrect use, false scientific conclusions or other problems using these programs. Please give proper reference when used).

(41) Consentino, F, and Claeskens, G. (2010). "Order selection tests with multiply-imputed data", Computational Statistics and Data Analysis, 54, 2284-2295. 

(40) Gijbels, I, Prosdocimi, I & Claeskens, G. (2010). "Nonparametric estimation of mean and dispersion functions in extended  generalized linear models", Test, 19, 580-608.

(39)  Krivobokova, T., Kneib, T. & Claeskens, G. (2010).  "Simultaneous confidence bands for penalized spline estimators", Journal of the American Statistical Association, 105, 852-863.

(38)  Tharmaratnam, K., Claeskens, G., Croux, C, & Salibian-Barrera, M. (2010). "S- Estimation for penalized regression splines". Journal of Computational and Graphical Statistics, 19(3), 609-625.       R-Software

(37) Claeskens, G. & Hart, J.D. (2009). "Rejoinder to: Goodness-of-fit tests in mixed models", Test, 18, 265-270.                                        See the journal website.

(36) Claeskens, G. & Hart, J.D. (2009). "Goodness-of-fit tests in mixed models", Test, 2009, 18, 213-239 .                                                    See the journal website.

(35)  Claeskens, G., Krivobokova, T. & Opsomer, J.D. (2009). "Asymptotic properties of penalized spline estimators", Biometrika,96, 529-544.

(34)  Kauermann, G., Claeskens, G. & Opsomer, J.D. (2009). "Bootstrapping for Penalized Spline Regression",
        Journal of Computational and Graphical Statistics, 18, 126-146.

(33)  Bissantz, N., Claeskens, G., Holzmann, H. & Munk, A. (2009). "Testing for lack of fit in inverse regression -   
     with applications to photonic imaging", Journal of the Royal Statistical Society, Series B, 71(1), 25-48.                                                          See journal website.

(32) Claeskens, G., Croux, C. & Van Kerckhoven, J. (2008). "An information criterion for variable selection in  
       Support Vector Machines". Journal of Machine Learning Research, 9, 541-558.

(31) Claeskens, G. & Consentino, F. (2008). " Variable selection with incomplete covariate data". Biometrics, 64, 1062-1096.

(30) Opsomer, J.D., Claeskens, G., Ranalli,M.G., Kauermann, G. & Breidt, F.J. (2008). "Nonparametric small area
      estimation using penalized spline regression", Journal of the Royal Statistical Society, Series B, 70, 265-286.

(29)  Claeskens, G. & Hjort, N.L. (2008). "Minimising average risk in regression models",  
    Econometric Theory, 24, 493-527.

(28) Claeskens, G., Croux, C. & Van Kerckhoven, J. (2007) "Prediction focussed model selection for  
      autoregressive models", The Australian and New Zealand Journal of Statistics, 49, 359-379.

(27) Claeskens, G. & Carroll, R.J. (2007). "An asymptotic theory for model selection inference in general
    semiparametric problems", Biometrika, 94, 249-265. Published version at the Biometrika website.

(26)  Claeskens, G., Croux, C. & Van Kerckhoven, J. (2006). "Variable selection for logistic regression 
     using a prediction focussed information criterion", Biometrics, 62, 972-979.

(25) Hjort, N.L. & Claeskens, G. (2006). "Focussed information criteria and model averaging for Cox's
    hazard regression model", Journal of the American Statistical Association, 101, 1449-1464.

(24) Claeskens, G., Nguti, R. & Janssen, P. (2008). "One-sided tests in shared frailty models". Test, 17, 69-82.
      The original publication is available at www.springerlink.com.

(23) Breidt, J., Claeskens, G. & Opsomer, J. (2005). "Model-assisted estimation for complex surveys
     using penalized splines". Biometrika, 92, 831-846.

(22) Crainiceanu, C., Ruppert, D., Claeskens, G. & Wand, M.P. (2005). Likelihood ratio tests of
    polynomial regression against a general nonparametric alternative. Biometrika, 92, 91-103.

(21) Aerts, M., Claeskens, G. & Hart, J.D. (2004). Bayesian motivated tests of function fit and
     their asymptotic frequentist properties. The Annals of Statistics, 32, 2580-2615.

(20) Claeskens, G. (2004). Restricted likelihood ratio lack of fit tests using mixed spline models,
    Journal of the Royal Statistical Society, Series B, 66, 909-926.

(19) Claeskens, G. & Hjort, N.L. (2004). Goodness of fit via nonparametric likelihood ratios,
      Scandinavian Journal of Statistics, 31, 487-513.

(18) Hjort, N.L. & Claeskens, G. (2003). Rejoinder to "The Focussed Information Criterion" and
      "Frequentist model average estimators", Journal of the American Statistical Association, 98, 938-945.

(17)  Hjort, N.L. & Claeskens, G. (2003). Frequentist model average estimators, Journal of the American
     Statistical Association, 98, 879-899 (with discussion).

(16)  Claeskens, G. & Hjort, N.L. (2003). The Focussed Information Criterion, Journal of the American
     Statistical Association, 98, 900-916 (with discussion).

(15) Claeskens, G., Jing, B.-Y., Peng, L. & Zhou, W. (2003). Empirical likelihood confidence regions
    for comparison distributions and ROC curves, The Canadian Journal of Statistics, 31, 173-190.

(14) Claeskens, G. & Van Keilegom, I. (2003). Bootstrap confidence bands for regression curves and
    their derivatives, The Annals of Statistics, 31, 1852-1884.

(13)  Claeskens, G., Aerts, M. & Molenberghs, G. (2002). A quadratic bootstrap method and
     improved estimation in logistic regression, Statistics and Probability Letters, 61, 383-394.

(12)  Claeskens, G., Aerts, M., Molenberghs, G. & Ryan, L. (2002). Robust benchmark dose
    determination based on profile score methods, Environmental and Ecological Statistics, 9, 357-377.

(11)  Claeskens, G. & Hall, P. (2002). Data sharpening for hazard rate estimation,
    Australian and New Zealand Journal of Statistics, 44, 277-283.

(10)  Claeskens, G. & Hall, P. (2002). Effect of dependence on stochastic measures of accuracy of
    density estimators, The Annals of Statistics, 30, 431-454.

(9)  Aerts, M., Claeskens, G., Hens, N. & Molenberghs, G. (2002). Local multiple imputation,
     Biometrika, 89, 375-388.

(8)  Aerts, M., Claeskens, G. & Wand, M. (2002). Some theory for penalized spline additive models,
    Journal of Statistical Planning and Inference, 103, 455-470.

(7)  Aerts, M. & Claeskens, G. (2001). Bootstrap tests for misspecified models, with application to
    clustered binary data, Journal of Computational Statistics and Data Analysis, 36, 383-401.

(6)  Claeskens, G. & Aerts, M. (2000). On local estimating equations in additive multiparameter models,
     Statistics and Probability Letters, 49, 139-148.

(5)  Aerts, M., Claeskens, G. & Hart, J.D. (2000). Testing lack of fit in multiple regression,
    Biometrika, 87, 405-424.

(4)  Claeskens, G. & Aerts, M. (2000). Bootstrapping local polynomial estimators in likelihood-based
    models, Journal of Statistical Planning and Inference, 86, 63-80.

(3)  Aerts, M., Claeskens, G. & Hart, J.D. (1999). Testing the fit of a parametric function,
    Journal of the American Statistical Association, 94, 869-879.

(2)  Aerts, M. & Claeskens, G (1999). Bootstrapping pseudolikelihood models for clustered binary data, Annals of the Institute of Statistical Mathematics, 51, 515-560.

(1) Aerts, M. & Claeskens, G. (1997). Local polynomial estimation in multiparameter likelihood models,  
    Journal of the American Statistical Association, 92, 1536-1545.

Book chapters

(8)  Flexible modelling of functional data using continuous wavelet dictionaries. Slaets, L., Claeskens, G. & Jansen, M. (2011) in Recent advances in functional data analysis and related topics, 297-300, Editor: F. Ferraty (Springer-Verlag, Berlin, Heidelberg).

(7) Model Selection, W. Zucchini, G. Claeskens & G. Nguefack-Tsague, in International Encyclopedia of Statistical Sciences, 2010, pp. 830-833, Editor: M. Lovric, Springer.

(6) Nonparametric Estimation,  G. Claeskens & M. Jansen, in International Encyclopedia of Statistical Sciences, 2010, pp. 959-962, Editor: M. Lovric, Springer.

(5) The Cramér-Rao Inequality, M. Jansen & G. Claeskens, in International Encyclopedia of Statistical Sciences, 2010, pp. 322-323, Editor: M. Lovric, Springer.

(4) Information Criteria, G.Claeskens, in Encyclopedia of Actuarial Sciences, 2003,
     Editors: J. Teugels & B. Sundt, Wiley.  

(3)  Model Misspecification, G. Claeskens, M. Aerts & L. Declerck, in: "Topics in Modelling of Clustered
    Data", 2002, pp. 173-194. Editors: Aerts, M., Geys, H., Molenberghs, G. & Ryan, L., Chapman & Hall/CRC.

(2)  Assessing the Fit of a Model, G. Claeskens & M. Aerts, in: "Topics in Modelling of Clustered Data",
    2002, pp. 139-156. Editors: Aerts, M., Geys, H., Molenberghs, G. & Ryan, L., Chapman & Hall/CRC.

(1)  Flexible Polynomial Models, M. Aerts, C. Faes & G. Claeskens, in:  "Topics in Modelling of
    Clustered Data", 2002, pp. 127-138. Editors: Aerts, M., Geys, H., Molenberghs, G. & Ryan, L., Chapman &     Hall/CRC.

In Proceedings

(13) Slaets, L. & Claeskens, G. (2011). Clustering Functional Data via Multivariate Functional Halfspace Depth. JSM Proceedings.

(12) Slaets, L., Claeskens, G. & Jansen, M. (2011). Flexible modelling of functional data using continuous wavelet dictionaries. Proceedings of the 26th International Workshop on Statistical Modelling, p. 561-564.

(11) Slaets, L, and Claeskens, G. (2010). Functional clustering based on multiresolution warping.
Proceedings of the 25th International Workshop on Statistical Modelling (Ed. A. Bowman), p. 501--504.

(10) Claeskens, G. (2007). Some recent advances in model selection.
    Proceedings of the 56th Session of the ISI, Lisboa (Portugal), Vol 20, p. 581-586.

(9) Consentino, F and Claeskens, G. (2007). Model selection with missing covariates under ignorable missingness.
    Proceedings of the 22nd International Workshop on Statistical Modelling (Ed. J. del Castillo, A. Espinal & P. Puig ), p.185-190.

(8) Claeskens, G. (2006). On focussed and less focussed model selection.
    Proceedings of the 21th International Workshop on Statistical Modelling. Galway (Ireland), p.3-13.

(7) Opsomer, J.D., Breidt, F.J., Claeskens, G., Kauermann, G. and Ranalli, G. (2004). Nonparametric small  
      area estimation using penalized spline regression. Proceedings of the Survey Research Methods
      Section, American Statistical Association, VA.

(6) Claeskens, G. and Hjort, N.L. (2003). Frequentist model averaging and model selection,
    Proceedings of the 17th International Workshop on Statistical Modelling (Ed. G. Verbeke,
     G. Molenberghs, M. Aerts & S. Fieuws), pp. 85-89.

(5) Aerts, M., Claeskens, G., Hart, J., Moons, E. & Wets, G. (2003). Two lack of fit tests for multiple
     logistic regression, Proceedings of the 17th International Workshop on Statistical Modelling
     (Ed. G. Verbeke, G. Molenberghs, M. Aerts & S. Fieuws), pp. 15-19.

(4)  Hens, N., Aerts, M., Claeskens, G. and Molenberghs, G. (2001). Multiple nonparametric bootstrap
    imputation, Proceedings of the 16th International Workshop on Statistical Modelling (Ed. Klein, B.
    and Korsholm, L.), pp. 219--226.

(3)  Aerts, M., Claeskens, G. & Molenberghs, G. (2000). Bootstrapping multiparameter models, with
    applications to clustered binary data, Proceedings of the 15th International Workshop on Statistical
    Modelling (Ed. V. Nunez-Anton & E. Ferreira), pp. 125-130.

(2)  Claeskens, G., Aerts, M. & Wand, M.P. (1999). Some results on penalized spline estimation in
    generalized additive and semiparametric models, Proceedings of the 52nd Session of  the
    International Statistical Institute, Helsinki, pp. 207-208.

(1)  Molenberghs, G.,  Geys, H.,  Declerck, L., Claeskens, G. & Aerts, M. (1998).
    Analysis of clustered multivariate data from developmental toxicity studies,
    Proceedings of the 13th Symposium on Computational Statistics, R. Payne & P. Green (eds), Bristol,
    Keynote paper, pp. 3-14.

My PhD thesis

Smoothing techniques and bootstrap methods for multiparameter likelihood models.

In compressed postcript format: go here
The 4 parts separately as postscript files: Intro, Part 1 Smooth curve estimation,
Part 2 Lack of fit tests, Part 3  Bootstrap procedures.