Asymptotics of kernel error density estimators in nonlinear autoregressive models
β Scribed by Keang Fu; Xiaorong Yang
- Publisher
- Springer
- Year
- 2008
- Tongue
- English
- Weight
- 142 KB
- Volume
- 44
- Category
- Article
- ISSN
- 0259-9791
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In the paper we prove rates of strong convergence of M-estimators for the parameters in a general nonlinear autoregressive model. In the proofs we utilize a variational principle from stochastic optimization theory which was proved by Shapiro (Ann. Oper. Res. 30 (1991) 169). The application of the g
In this paper, we give the exact asymptotic L 1 -error for the kernel estimator of the density function from censored data. We also give asymptotically optimal bandwidths. Strong approximation of the product-limit estimator by a Gaussian process is used to obtain the result.
In this paper we consider the weighted average square error A,(rc)= (l/n)~=1 {f"(3))f(Xj)}2~(Xj), where f is the common density function of the independent and identically distributed random vectors X~ ..... X,, f, is the kernel estimator based on these vectors and ~z is a weight function. Using U-s