1 smoothing Topics

[1]Efromovich, S. (1999). Nonparametric Curve Estimation: Methods, Theory
and Applications. Springer-Verlag. New York, NY.
[2]Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications.
Chapman and Hall. New York, NY.
[3]Green, P. J. and Silverman, B. W. (1994). Nonparametric regression
and generalized linear models: a roughness penalty approach. Chapman and
Hall. New York, NY.
[4]H¨ardle, W. (1990). Applied Nonparametric Regression. Cambridge University
Press. Cambridge.
[5]Hastie, T. and Tibshirani, R. (1999). Generalized Additive Models. Chapman
and Hall. New York, NY.
[6]Ingster, Y. and Suslina, I. (2003). Nonparametric Goodness-of-Fit Testing
Under Gaussian Models. Springer-Verlag. New York, NY.
[7]Loader, C. (1999a). Local Regression and Likelihood. Springer-Verlag. New
York, NY.
[8]McCullagh, P. and Nelder, J. A. (1999). Generalized linear models.
Chapman and Hall. New York, NY.
[9]Ruppert, D., Wand, M. and Carroll, R. (2003). Semiparametric Regression.
Cambridge University Press. Cambridge.
[10]Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice,
and Visualization. Wiley. New York, NY.
[11]Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis.
Chapman and Hall. New York, NY.
[12]Simonoff, J. S. (1996). Smoothing Methods in Statistics. Springer-Verlag.
New York, NY.

2 Bootstrap Topics

[13]Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and Their
Application. Cambridge University Press. Cambridge.
[14]Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap.
Chapman and Hall. New York, NY.
[15]Hall, P. (1992a). The Bootstrap and Edgeworth Expansion. Springer-Verlag.
New York, NY.
[16]Shao, J. and Tu, D. (1995). The Jackknife and Bootstrap. Springer-Verlag.
New York, NY.

3 Wavelets Topics

[17]Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM. New York, NY.
[18]Ogden, R. T. (1997). Essential Wavelets for Statistical Applications and
Data Analysis. Birkh¨auser. Boston, MA.
[19]H¨ardle, W., Kerkyacharian, G., Picard, D. and Tsybakov, A. (1998).
Wavelets, Approximation, and Statistical Applications. Springer-Verlag.
New York, NY.

4 Statistical Learning Ttopics

[20]Devroye, L., Gy¨orfi, L. and Lugosi, G. (1996). A Probabilistic Theory
of Pattern Recognition. Springer-Verlag. New York, NY.
[21]Hastie, T., Tibshirani, R. and Friedman, J. H. (2001). The Elements
of Statistical Learning: Data Mining, Inference, and Prediction. Springer-
Verlag. New York, NY.

5 Nonparametric Bayesian Topics

[22]Dey, D., Muller, P. and Sinha, D. (1998). Practical Nonparametric and
Semiparametric Bayesian Statistics. Springer-Verlag. New York, NY.
[23]Ghosh, J. and Ramamoorthi, R. (2003). Bayesian Nonparametrics.
Springer-Verlag. New York, NY.

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