CONTENTS
SYNOPSIS 1
1. The object of the study
2. The kernel density estimator
3. The kernel regression estimator and the induced predictor
4. IvIixing processes  
5. Density estimation
   6. Regression estimation and Prediction
7. Implementation of nonparametric method
CHAPTER 1. Inequalities for mixing processes
1. Mixing
2. Coupling
3. Inequalities for covariances and joint densities
4. Exponential type inequalities
5. Some limit theorems for strongly mixing processes
Notes
CHAPTER 2. Density estimation for discrete time
processes
1. Density estimation
2. Optimal asymptotic quadratic error
3. Uniform almost sure convergence
4. Asymptotic normality
5. Non regular cases
Notes
CHAPTER 3. Regression estimation and prediction
for discrete time processes
1. Regression estimation
      2. Asymptotic behaviour of the regression estimator
3. Prediction for a stationary l\Iarkov process of order k
4. Prediction for general processes
5. Implementation of non parametric method

CHAPTER 4. Density estimation for continuous
time processes
2. Optimal and superoptimal asymptotic quadratic error
3. Optimal and superoptimal uniform convergence rates
4. Sampling
Notes
CHAPTER 5. Regression estimation and prediction
in continuous time
1. The kernel regression estimator in continuous time
2. Optimal asymptotic quadratic error
3. Superoptimal asymptotic quadratic error
4. Limit in distribution
5. Uniform convergence rates
6. Sampling
7. Nonparametric prediction in continuous time


Appendix - Numerical results

Index