The family of rank estimators, including Han's maximum rank correlation (Han, 1987) as a
notable example, has been widely exploited in studying regression problems. For these …

This paper extends the linearized maximum rank correlation (LMRC) estimation proposed
by Shen et al.(2023) to the setting where the covariate is a function. However, this extension …

The purpose of this paper is the presentation of distribution theory for generic estimators
based on the pairwise comparison of observations in problems where identification is …

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 …

Conditional mean independence (CMI) is one of the most widely used assumptions in the
treatment effect literature to achieve model identification. We propose a Kolmogorov …

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 …

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 …

Consider the partially linear model (PLM) with random design: Y= XTβ∗+ g (W)+ u, where g
(·) is an unknown real-valued function, X is p-dimensional, W is one-dimensional, and β∗ is …

Consider a censored heteroscedastic transformation regression model where both the
transformation function and the error distribution function are completely unknown. A method …