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20 kwi 2022 · An extremum estimator is an estimator that maximizes (or minimizes) an objective function. An m-estimator is an extremum estimator in which the objective function is a sample average. Minimum distance is on section 8.5 of Bruce Hansen's textbook.
Minimum-distance estimation (MDE) is a conceptual method for fitting a statistical model to data, usually the empirical distribution. Often-used estimators such as ordinary least squares can be thought of as special cases of minimum-distance estimation.
This chapter will present a number of fairly general results on parameter estimation. We begin with perhaps the oldest formalized theory of estimation, the classical theory of the method of moments.
This verifies that the minimum distance estimator is consistent and asymptotically unbiased and, even though it is not likely to have min- imum variance, at least we will be able to estimate the variance.
13 lut 2019 · The two-stage least-squares (2SLS) instrumental-variables (IV) estimator for the parameters in linear models with a single endogenous variable is shown to be identical to an optimal minimum-distance (MD) estimator based on the individual instrument-specific IV estimators.
1 paź 1994 · Unweighted minimum distance estimators in a location-scale model are investigated. They are derived from the Cramér-von Mises goodness of fit measure.
1 kwi 2011 · This paper introduces a general method for the numerical derivation of a minimum distance (MD) estimator for the parameters of an unknown distribution. The approach is based on an active sampling of the space in which the random sample takes values and on the optimization of the parameters of a suitable approximating model.
