We propose a procedure for estimating the critical values of the extended Kolmogorov- Smirnov tests of First and Second Order Stochastic Dominance in the general K-prospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence...

We propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of Stochastic Dominance of arbitrary order in the general K-prospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate "general" dependence...

This paper is concerned with the nonparametric estimation of regression quantiles where the response variable is randomly censored. Using results on the strong uniform convergence of U-processes, we derive a global Bahadur representation for the weighted local polynomial estimators, which is...

We propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of Stochastic Dominance of arbitrary order in the general K-prospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence...

We propose a procedure for estimating the critical values of the Klecan, McFadden, and McFadden (1990) test for first and second order stochastic dominance in the general k-prospect case. Our method is based on subsampling bootstrap. We show that the resulting test is consistent. We allow for...

We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary processes {(<italic>Y</italic>, <italic>null</italic>)}. We establish a strong uniform consistency rate for the Bahadur representation of estimators of the regression function and its derivatives. These results are...

We propose a procedure for estimating the critical values of the extended Kolmogorov- Smirnov tests of Stochastic Dominance of arbitrary order in the general K-prospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence...

In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators of the finite dimensional parameters can achieve root-n consistency. The requirement of under-smoothing may result as we show from inefficient estimation methods or technical...

We propose a procedure for estimating the critical values of the Klecan, McFadden, and McFadden (1990) test for first and second order stochastic dominance in the general k-prospect case. Our method is based on subsampling bootstrap. We show that the resulting test is consistent. We allow for...