A general framework is proposed for (auto)regression nonparametric estimationof recurrent time series in a class of Hilbert Markov processes with a Lipschitzconditional mean. This includes various nonstationarities by relaxing usual dependenceassumptions as mixing or ergodicity, which are...

This paper investigates the bias and the Bahadur representation of a local polynomial estimator of the conditional quantile function and its derivatives. The bias and Bahadur remainder term are studied uniformly with respect to the quantile level, the covariates and the smoothing parameter. The...

We derive the limit distribution of the number of crossings of a level by a random walk with continuously distributed increments, using a Brownian motion local time approximation. This complements the well-known result for the random walk on the integers. Use of the frequency of level crossings...

This paper studies a semiparametric nonstationary binary choice model. Imposing a spherical normalization constraint on the parameter for identification purpose, we find that the MSE and SMSE are at least sqrt(n)-consistent. Comparing this rate to the parametric MLE’s convergence rate, we show...

We propose a general procedure for testing that a regression function has a prescribed parametric form. We allow for multivariate regressors, non-normal errors and heteroscedasticity of unknown form. The test relies upon a nonparametric linear estimation method, such as a sieves expansion or the...