Add to ORKGKlugman and Parsa have introduced the theory underlying minimum distance estimation with parametric distributions. In this review, I develop their ideas further to provide a more complete view of the characteristics of minimum distance es
One natural way to measure model adequacy is by using statistical distances as loss functions. A rel...
It is shown that (under some regularity conditions) minimum distance estimators for a (possibly mult...
We study the asymptotic properties of a general class of minimum distance estimators based on L2 nor...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
Semiparametric minimum-distance estimation methods are introduced for the estimation of parametric o...
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...
summary:The paper deals with sufficient conditions for the existence of general approximate minimum ...
Given an exponential distribution g(x) and the information in terms of moments of the random variabl...
Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the pa...
This paper proposes a class of minimum distance estimators for the underlying parameters in a Markov...
AbstractThis paper introduces a general method for the numerical derivation of a minimum distance (M...
This paper is concerned with the study of some properties of the distance between statistical indivi...
Abstract: The probability function of a discrete distribution belonging to Sundt's family satis...
A robust estimator introduced by Beran (1977a, 1977b)?Mich i based on the minimum Hellinger distance...
One natural way to measure model adequacy is by using statistical distances as loss functions. A rel...
It is shown that (under some regularity conditions) minimum distance estimators for a (possibly mult...
We study the asymptotic properties of a general class of minimum distance estimators based on L2 nor...
Loss distributions have a number of uses in the pricing and reserving of casualty insurance. Many au...
Semiparametric minimum-distance estimation methods are introduced for the estimation of parametric o...
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...
summary:The paper deals with sufficient conditions for the existence of general approximate minimum ...
Given an exponential distribution g(x) and the information in terms of moments of the random variabl...
Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the pa...
This paper proposes a class of minimum distance estimators for the underlying parameters in a Markov...
AbstractThis paper introduces a general method for the numerical derivation of a minimum distance (M...
This paper is concerned with the study of some properties of the distance between statistical indivi...
Abstract: The probability function of a discrete distribution belonging to Sundt's family satis...
A robust estimator introduced by Beran (1977a, 1977b)?Mich i based on the minimum Hellinger distance...
One natural way to measure model adequacy is by using statistical distances as loss functions. A rel...
It is shown that (under some regularity conditions) minimum distance estimators for a (possibly mult...
We study the asymptotic properties of a general class of minimum distance estimators based on L2 nor...