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AuthorBosq, D. author
TitleNonparametric Statistics for Stochastic Processes [electronic resource] : Estimation and Prediction / by D. Bosq
ImprintNew York, NY : Springer US, 1996
Connect tohttp://dx.doi.org/10.1007/978-1-4684-0489-0
Descript 1 illus. in color. online resource

CONTENT

Synopsis -- 1. The object of the study -- 2. The kernel density estimator -- 3. The kernel regression estimator and the induced predictor -- 4. Mixing processes -- 5. Density estimation -- 6. Regression estimation and Prediction -- 7. Implementation of nonparametric method -- 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 -- 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 -- 3. Regression estimation and prediction for discrete time processes -- 1. Regression estimation -- 2. Asymptotic behaviour of the regression estimator -- 3. Prediction for a stationary Markov process of order k -- 4. Prediction for general processes -- 5. Implementation of nonparametric method -- Notes -- 4. Density estimation for continuous time processes -- 1. The kernel density estimator in continuous time -- 2. Optimal and superoptimal asymptotic quadratic error -- 3. Optimal and superoptimal uniform convergence rates -- 4. Sampling -- Notes -- 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 -- Notes -- Appendixโ{128}{148}Numerical results


Mathematics Probabilities Mathematics Probability Theory and Stochastic Processes



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