Specifying statistical models : from parametric to non-parametric, using Bayesian or non-Bayesian approaches /
Guardado en:
| Autor Corporativo: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro |
| Idioma: | English |
| Publicado: |
New York :
Springer-Verlag,
c1983.
|
| Series: | Lecture notes in statistics (Springer-Verlag) ;
v. 16. |
| Materias: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Tabla de Contenidos:
- Léopold Simar, Protecting against gross errors: the aid of Bayesian methods
- A. F. M. Smith, Bayesian approaches to outliers and robustness
- J.-P. Raoult, D. Criticou and D. Terzakis, The probability integral transformation for nonnecessary absolutely continuous distribution functions, and its application to goodness-of-fit tests
- Paul Doukhan, Simulation in the general first order autoregressive process (unidimensional normal case)
- Denis Bosq, Nonparametric prediction in stationary processes
- J.-P. Florens, Approximate reductions of Bayesian experiments
- M. Mouchart and L. Simar, Theory and applications of least squares approximation in Bayesian analysis
- J.-M. Rolin, Nonparametric Bayesian statistics: a stochastic process approach
- Lucien Birgé, Robust testing for independent nonidentically distributed variables and Markov chains
- A. Hillion, On the use of some variation distance inequalities to estimate the difference between sample and perturbed sample
- Jacques Benasseni, A contribution to robust principal component analysis
- Gérard Collomb, From nonparametric regression to nonparametric prediction: survey of the mean square error and original results on the predictogram.