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523
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
Abstract

Cited by 185 (19 self)
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extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Minimax Estimation via Wavelet Shrinkage
, 1992
"... We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coe cients. The shrinkage can be tuned to be nearly minim ..."
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Cited by 321 (29 self)
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minimax over any member of a wide range of Triebel and Besovtype smoothness constraints, and asymptotically minimax over Besov bodies with p q. Linear estimates cannot achieve even the minimax rates over Triebel and Besov classes with p <2, so our method can signi cantly outperform every linear
Extremum sieve estimation in koutofn systems Extremum Sieve Estimation in koutofn Systems. 1
"... ABSTRACT The paper considers nonparametric estimation of absolutely continuous distribution functions of lifetimes of nonidentical components in koutofn systems from the observed "autopsy" data. In economics, ascending "button" or "clock" auctions with n heterogene ..."
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heterogeneous bidders present 2outofn systems. Classical competing risks models are examples of noutofn systems. Under weak conditions on the underlying distributions the estimation problem is shown to be wellposed and the suggested extremum sieve estimator is proven to be consistent. The paper
Extremum estimation and numerical derivatives
, 2010
"... Abstract Many empirical researchers rely on the use of finitedifference approximation to evaluate derivatives of estimated functions. For instance commonly used optimization routines implicitly use finitedifference formulas for the gradients, which require the choice of step size parameters This ..."
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Cited by 4 (0 self)
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This paper investigates the statistical properties of numerically evaluated gradients and of extremum estimators computed using numerical gradients. We find that first, one needs to adjust the step size or the tolerance as a function of the sample size. Second, higherorder finite difference formulas reduce
Maximization by Parts in Extremum Estimation
, 2007
"... In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the NewtonRalphson algorithm is difficult, if not impossible. While the NewtonRalphson algorithm makes use of the full Hessian matrix which may be diffi ..."
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Cited by 2 (0 self)
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In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the NewtonRalphson algorithm is difficult, if not impossible. While the NewtonRalphson algorithm makes use of the full Hessian matrix which may
Maximization by Parts in Extremum Estimation
, 2006
"... In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the NewtonRalphson algorithm is di ¢ cult, if not impossible. While the NewtonRalphson algorithm makes use of the full Hessian matrix which may be di ¢ ..."
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In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the NewtonRalphson algorithm is di ¢ cult, if not impossible. While the NewtonRalphson algorithm makes use of the full Hessian matrix which may be di
Extremum Estimation and Numerical Derivatives
, 2010
"... Many empirical researchers rely on the use of finitedifference approximation to evaluate derivatives of estimated functions. For instance many optimization routines implicitly use finitedifference formulas for the gradients. Such routines frequently require the choice of step size parameters for fi ..."
Abstract
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for finitedifference numerical gradients or similar parameters such as the computing tolerance. This paper investigates the statistical properties numerically evaluated gradients and the properties of extremum estimators computed using numerical gradients. We find that first, for unbiased inference one
Extremum Estimation and Numerical Derivatives
, 2009
"... Many empirical researchers rely on the use of numerical optimization routines to compute extremum estimators. Such routines frequently require the choice of step size parameters for finitedifference numerical gradients or similar parameters such as the computing tolerance. This paper investigates ..."
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Many empirical researchers rely on the use of numerical optimization routines to compute extremum estimators. Such routines frequently require the choice of step size parameters for finitedifference numerical gradients or similar parameters such as the computing tolerance. This paper investigates
A Bayesian Interpretation of Extremum Estimators
, 1997
"... Extremum estimation is typically an ad hoc semiparametric estimation procedure which is only justified on the basis of the asymptotic properties of the estimators. For a fixed finite data set, consider a large number of investigations using different extremum estimators to estimate the same paramet ..."
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Extremum estimation is typically an ad hoc semiparametric estimation procedure which is only justified on the basis of the asymptotic properties of the estimators. For a fixed finite data set, consider a large number of investigations using different extremum estimators to estimate the same
Results 1  10
of
523