Summary 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 component, and the model is characterized by an unknown monotone link function. We propose weighted rank estimation procedures for estimating (a) the coefficients for the linear component, (b) the non-parametric component (and its derivative) and (c) the average derivative for the non-parametric component. The method is applied to study the non-linear relationship between household income and children's cognitive development. © 2011 The Author(s). The Econometrics Journal © 2011 Royal Economic Society
**Research partially supported by OnR contract N00014-80-C-0741. ApprW for p e r Afja&MW The cur...
This note shows that the asymptotic variance of Chen’s [Chen, S., 2002. Rank estimation of transform...
AbstractReduced rank regression assumes that the coefficient matrix in a multivariate regression mod...
Preliminary Do not distribute We consider a generalized regression model with a partially linear ind...
The typical generalized linear model for a regression of a response Y on predictors (X, Z) has condi...
We study the non-parametric estimation of partially linear generalized single-index functional model...
In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimati...
[[abstract]]For partial spline models with a monotone nonlinear component, a class of monotone estim...
Single-index models are popular regression models that are more flexible than linear models and stil...
The paper considers estimation of a model.b; = D F ( x//3,, u,), where the composite transforma-ti...
A natural generalization of the well known generalized linear models is to allow only for some of th...
Partial linear models, a family of popular semiparametric models, provide us with an interpretable a...
This paper considers estimation of the unknown linear index coe ¢ cients of a model in which a num-b...
We propose a new estimator, called the Generalized Maximum Rank Correlation Estimator (GMRC), of the...
Summary Most dimension reduction models are suited for continuous but not for discrete covariates. A...
**Research partially supported by OnR contract N00014-80-C-0741. ApprW for p e r Afja&MW The cur...
This note shows that the asymptotic variance of Chen’s [Chen, S., 2002. Rank estimation of transform...
AbstractReduced rank regression assumes that the coefficient matrix in a multivariate regression mod...
Preliminary Do not distribute We consider a generalized regression model with a partially linear ind...
The typical generalized linear model for a regression of a response Y on predictors (X, Z) has condi...
We study the non-parametric estimation of partially linear generalized single-index functional model...
In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimati...
[[abstract]]For partial spline models with a monotone nonlinear component, a class of monotone estim...
Single-index models are popular regression models that are more flexible than linear models and stil...
The paper considers estimation of a model.b; = D F ( x//3,, u,), where the composite transforma-ti...
A natural generalization of the well known generalized linear models is to allow only for some of th...
Partial linear models, a family of popular semiparametric models, provide us with an interpretable a...
This paper considers estimation of the unknown linear index coe ¢ cients of a model in which a num-b...
We propose a new estimator, called the Generalized Maximum Rank Correlation Estimator (GMRC), of the...
Summary Most dimension reduction models are suited for continuous but not for discrete covariates. A...
**Research partially supported by OnR contract N00014-80-C-0741. ApprW for p e r Afja&MW The cur...
This note shows that the asymptotic variance of Chen’s [Chen, S., 2002. Rank estimation of transform...
AbstractReduced rank regression assumes that the coefficient matrix in a multivariate regression mod...