In this paper a new nonparametric estimate of conditional quantiles is proposed, that avoids the problem of crossing quantile curves [calculated for various p ∈ (0, 1)]. The method uses an initial estimate of the conditional distribution function in a first step and solves the problem of inversion and monotonization with respect to p ∈ (0, 1) simultaneously. It is demonstrated that the new esti...

This paper considers a class of nonparametric autoregression models with nonstationarity in the mean and then a class of nonparametric time series regression models with nonstationarity in both the conditional mean and conditional variance. For the nonparametric autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression ...

We propose an efficient semiparametric estimator for the multivariate linear quantile regression model in which the conditional joint distribution of errors given regressors is unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotically as efficient as if the conditional distribution were known. Simu...

The copula-vine method of specifying dependence in high dimensional distributions has been developed in Cooke [1], Bedford and Cooke [6], Kurowicka and Cooke ([2], [4]), and Kurowicka et all [3]. According to this method, a high dimensional distribution is constructed from two dimensional and conditional two dimensional distributions of uniform variaties. When the (conditional) two dimensional ...

Conditional Poisson models have been used to analyze vaccine safety data from self-controlled case series (SCCS) design. In this paper, we derived the likelihood function of fixed effects models in analyzing SCCS data and showed that the likelihoods from fixed effects models and conditional Poisson models were proportional. Thus, the maximum likelihood estimates (MLEs) of time-varying variables...

In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained frommany clusters. As the variable selection procedure, we here use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC...

We propose non-nested tests for competing conditional moment resctriction models using a method of empirical likelihood. Our tests are based on the method of conditional empirical likelihood developed by Kitamura, Tripathi and Ahn (2004) and Zhang and Gijbels (2003). By using the conditional implied probabilities, we develop three non-nested tests: the moment encompassing, Cox-type, and effcien...

Traditional estimators for nonparametric frontier models (DEA, FDH) are very sensitive to extreme values/outliers. Recently, Aragon, Daouia, and Thomas-Agnan (2005) proposed a nonparametric α-frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values/outliers. Their estimator is based on a nonsmooth empirical conditional distribution. In...

The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event...

We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model — with a conditional quantile restriction for each equation — in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotical...