Based on the smoothed partial rank (SPR) loss function, we propose a group LASSO
penalized SPR estimator for the varying coefficient nonparametric transformation models …

We consider a generalized regression model with a partially linear index. The index
contains an additive non‐parametric component in addition to the standard linear …

In this paper we provide a computation algorithm to get a global solution for the maximum
rank correlation estimator using the mixed integer programming (MIP) approach. We …

This study examines identification and estimation in a correlated random coefficients (CRC)
model with an unknown transformation of the dependent variable, namely, where the latent …

Early studies of the generalized transformation panel data model resorted to the identical
marginal distribution of the error term over time. This stationarity condition is restrictive for …

We study a semi-/nonparametric regression model with a general form of nonclassical
measurement error in the outcome variable. We show equivalence of this model to a …

Rank regression is a robust modeling tool; it is challenging to implement it for the distributed
massive data owing to memory constraints. In practice, the massive data may be distributed …

This article is concerned with semiparametric estimation of a partially linear transformation
model under conditional quantile restriction with no parametric restriction imposed either on …

This paper considers the estimation of a Box-Cox transformation model with varying
coefficient. A two-step approach is proposed in which the first step estimates the varying …

In this paper, we consider rank estimation for partial functional linear regression models
based on functional principal component analysis. The proposed rank-based method is …