In this article, we propose a multivariate Pascal mixture regression model as an alternative to understand the association between multivariate count response variables and their covariates. When compared to the copula approach, this proposed class of regression models is not only less complex...

This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for high dimensional nonlinear factor models, with slightly stronger conditions on the relative magnitude of N(number of subjects) and T(number of time periods). Factors and loadings are estimated by maximum...

In this paper, we investigate the nonlinear quantile regression with mixed discrete and continuous regressors. A local linear smoothing technique with the mixed continuous and discrete kernel function is proposed to estimate the conditional quantile regression function. Under some mild...

This paper reestablishes the main results in Bai (2003) and Bai and Ng(2006) for generalized factor models, with slightly stronger conditions on therelative magnitude of N(number of subjects) and T(number of time periods).Convergence rates of the estimated factor space and loading space and...

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal...