Sentence examples for minimum distance estimator from inspiring English sources

We propose a jackknife minimum distance estimator designed to reduce the finite-sample bias of the optimal minimum distance estimator.

A simulation study demonstrates these characteristics of the minimum distance estimator.

Monte Carlo results indicate that our jackknife minimum distance estimator is a promising alternative to existing minimum distance procedures.

We propose a minimum distance estimator that is at least as efficient as CON, yet consistent even when partial effects are present.

The empirical exercise employs a minimum distance estimator (MDE) such that the distance between the KM empirical survival function and the one predicted by the theoretical model is minimised.

This stage consists of solving a quadratic optimization model by imposing monotonicity on parameters via the asymptotically equivalent minimum distance estimator, together with the parameters of the production frontier, β ∧, and their covariance matrix, Ω ∧ β, which are extracted from the first step: β 0 ∧ = arg min β 0 ∧ − β ∧ Ω β ‐ 1 β 0 ∧ − β ∧ : f i x, β 0 ∧ ≥ 0 ∀ i, x (5).

For a more comprehensive discussion of the properties of minimum distance estimators, we refer to Millar [21].

The paper establishes the asymptotic normality of the proposed test statistics and that of the corresponding minimum distance estimators under the fitted model.

This paper discusses the asymptotic behavior of Koul's minimum distance estimators of the regression parameter vector in linear regression models with long memory moving average errors, when the design variables are known constants.

Kolomogorov-Smirnov test is a form of minimum distance estimation.

5, 445 463] investigated the problem of minimum Hellinger distance procedure (MHDP) for continuous data and showed that minimum Hellinger distance estimator (MHDE) of a finite dimensional parameter is as efficient as the MLE (maximum likelihood estimator) under a true model assumption.