robustness and obtain the asymptotic distribution of the M estimator through the functional approach. As a consequence, our …

asymptotic distribution, we also obtain robustness results for our estimator. All of our results are valid for a broad class of ß …

robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the … robustness (when estimated by kernel). Our results are illustrated performing Monte-Carlo simulation. …

We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap process. To this … sequence. This is achieved by using well-established coupling techniques. Then we apply the result to the bootstrap process and … bootstrap process has the desired properties. Moreover, by using some 'decoupling' argument, we show that the stationary density …

bootstrap versions of the tests have much better properties in this respect. In other words, the bootstrap can be used to size …

bootstrap resampling technique. The method is illustrated on S&P 500 index data. …

use quantities derived from the parameters to disentangle the relationships between the variables. Bootstrap methods are … often used for inference on the derived quantities. Alternative bootstrap methods for this purpose are discussed, some …

often not provided. If confidence intervals are given they are often based on bootstrap methods with poor theoretical …

fixed link functions. In this paper we consider a bootstrap specification test that detects nonparametric deviations of the … link function. The bootstrap is used with the aim to find a more accurate distribution under the null than the normal … we show that the bootstrap is able to capture the negative bias and the skewness of the test statistic. It yields better …