Semiparametric minimum-distance estimation methods are introduced for the estimation of parametric or semiparametric econometric models. The semiparametric minimum-distance estimation methods share some familiar properties of the classical minimum-distance estimation method. However, they can be applied to the estimation of models with disagregated data. Asymptotic properties of the estimators are analyzed. Some goodness-of-fit test statistics are introduced. For the estimation of some econometric models, weighted minimum-distance estimators can be asymptotically efficient. The minimum-distance estimators are asymp-totically invariant with respect to some transformations
We propose and investigate a new estimation method for the parameters of models consisting of smooth...
This paper proposes a class of minimum distance estimators for the underlying parameters in a Markov...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
consistent and asymptotically normally distributed. Copyright (C) 2010 The Author(s). The Econometri...
We study the asymptotic properties of a general class of minimum distance estimators based on L2 nor...
It is well known that the maximum likelihood fit of the logistic regression parameters can be greatl...
A technique based on minimum distance, derived from a coefficient of determination and representable...
AbstractThe article considers estimating a parameter θ in an imprecise probability model (P¯θ)θ∈Θ wh...
A general method for selecting the tuning parameter in minimum distance estimators is proposed. The ...
AbstractWe investigate the estimation problem of parameters in a two-sample semiparametric model. Sp...
AbstractThis paper introduces a general method for the numerical derivation of a minimum distance (M...
A covariance-stationary vector of variables has a Wold representation whose coefficients can be semi...
AbstractMinimum distance techniques have become increasingly important tools for solving statistical...
Klugman and Parsa have introduced the theory underlying minimum distance estimation with parametric ...
Abstract. A general class of minimum distance estimators for continuous models called minimum dispar...
We propose and investigate a new estimation method for the parameters of models consisting of smooth...
This paper proposes a class of minimum distance estimators for the underlying parameters in a Markov...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
consistent and asymptotically normally distributed. Copyright (C) 2010 The Author(s). The Econometri...
We study the asymptotic properties of a general class of minimum distance estimators based on L2 nor...
It is well known that the maximum likelihood fit of the logistic regression parameters can be greatl...
A technique based on minimum distance, derived from a coefficient of determination and representable...
AbstractThe article considers estimating a parameter θ in an imprecise probability model (P¯θ)θ∈Θ wh...
A general method for selecting the tuning parameter in minimum distance estimators is proposed. The ...
AbstractWe investigate the estimation problem of parameters in a two-sample semiparametric model. Sp...
AbstractThis paper introduces a general method for the numerical derivation of a minimum distance (M...
A covariance-stationary vector of variables has a Wold representation whose coefficients can be semi...
AbstractMinimum distance techniques have become increasingly important tools for solving statistical...
Klugman and Parsa have introduced the theory underlying minimum distance estimation with parametric ...
Abstract. A general class of minimum distance estimators for continuous models called minimum dispar...
We propose and investigate a new estimation method for the parameters of models consisting of smooth...
This paper proposes a class of minimum distance estimators for the underlying parameters in a Markov...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...