The Empirical distribution Function

Bias estimation

Estimation  of standard error, variance and mean squared error

Confidence Intervals using Bootstrapping

Use of bootstrapping in Linear Regression

 The Jackknife and its applications

Jacknife-after-bootstrap

The Cross-validation method  to estimate the prediction error

    Splitting Rules

            Overfitting and pruning trees

            Decision Trees Algorithms: Rpart, CART,  C4.5

            Decision trees for regression and classification

           Histograms 

            Kernel density estimation

            Kernel density estimation for regression

            Kernel density estimation for classification

            The naive Bayes classifier

Review of Markov Chain

            Metropolis/Hasting algorithms

            The Gibbs Sampler.

1.Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.

2. Davison, A. C., & Hinkley, D. V. (1997). Bootstrapping methods and their application. Cambridge, UK: Cambridge University Press.

3. Hastie, T, Tibshirani, R & Friedman, J. (2001). Elements of Statistical Learning. Springer-verlag.

5. Martinez, W.L, & Martinez, A. (2002). Computational Staistics handbook with MATLAB. Chapman and Hall/CRC, Boca Raton. Florida.  

6. Liu, J. (2001). Monte Carlo Strategies in Scientific Computing. Springer Verlag

7. Additional papers by E. Acuña, L. Breiman, R. Kohavi,. R. Tibshirani, T. Hastie and others.

1         Partial  Exam                                            30%

2          Homework (3)                                         30%

3         Project including oral presentation           20%

4.    Final (Take-home)                                   20%