The Experts below are selected from a list of 10074 Experts worldwide ranked by ideXlab platform
Jinyong Hahn - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic Variance of semiparametric estimators with generated regressors
Econometrica, 2013Co-Authors: Jinyong Hahn, Geert RidderAbstract:We study the Asymptotic distribution of three-step estimators of a finite-dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the first step. The first-step estimator is either parametric or nonparametric. Using Newey's (1994) path-derivative method, we derive the contribution of the first-step estimator to the influence function. In this derivation, it is important to account for the dual role that the first-step estimator plays in the second-step nonparametric regression, that is, that of conditioning variable and that of argument.
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a practical Asymptotic Variance estimator for two step semiparametric estimators
The Review of Economics and Statistics, 2011Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong HahnAbstract:The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if"it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
Johan Lyhagen - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic properties of spearman s rank correlation for variables with finite support
PLOS ONE, 2016Co-Authors: Petra Ornstein, Johan LyhagenAbstract:The Asymptotic Variance and distribution of Spearman’s rank correlation have previously been known only under independence. For variables with finite support, the population version of Spearman’s rank correlation has been derived. Using this result, we show convergence to a normal distribution irrespectively of dependence, and derive the Asymptotic Variance. A small simulation study indicates that the Asymptotic properties are of practical importance.
Marvin K. Nakayama - One of the best experts on this subject based on the ideXlab platform.
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Confidence intervals for quantiles with standardized time series
2013 Winter Simulations Conference (WSC), 2013Co-Authors: James M. Calvin, Marvin K. NakayamaAbstract:Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (CIs) for the steady-state mean of a stationary process. STS techniques cancel out the Variance constant in the Asymptotic distribution of the centered and scaled estimator, thereby eliminating the need to consistently estimate the Asymptotic Variance to obtain a CI. This is desirable since estimating the Asymptotic Variance in steady-state simulations presents nontrivial challenges. Difficulties also arise in estimating the Asymptotic Variance of a quantile estimator. We show that STS methods can be used to build CIs for a quantile for the case of crude Monte Carlo (i.e., no Variance reduction) with independent and identically distributed outputs. We present numerical results comparing CIs for quantiles using STS to other procedures.
Giorgio Picci - One of the best experts on this subject based on the ideXlab platform.
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ESTIMATING THE Asymptotic Variance OF CLOSED LOOP SUBSPACE ESTIMATORS
IFAC Proceedings Volumes, 2020Co-Authors: Alessandro Chiuso, Giorgio PicciAbstract:Abstract Subspace identification for closed loop systems has been recently studied by several authors. Recent results are available which express the Asymptotic Variance of the estimated parameters (and hence of any system invariant) as a function of the “true„ underlying system parameters and of certain conditional coVariance matrices. When it comes to using these formulas in practice one is faced with the problem of computing an estimator for the Variance from input-output data alone. In this paper we discuss this problem, we propose an algorithm which computes an estimate of the Variance from data alone and we show, through some simple simulation examples, how this estimate behaves as compared both to the “true„ Asymptotic Variance and to its Monte Carlo estimate.
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numerical conditioning and Asymptotic Variance of subspace estimates
Automatica, 2004Co-Authors: Alessandro Chiuso, Giorgio PicciAbstract:New formulas for the Asymptotic Variance of the parameter estimates in subspace identification, show that the accuracy of the parameter estimates depends on certain indices of 'near collinearity' of the state and future input subspaces of the system to be identified. This complements the numerical conditioning analysis of subspace methods presented in the companion paper (On the ill-conditioning of subspace identification with inputs, Automatica, doi:10.1016/j.automatica.2003.11.009).
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the Asymptotic Variance of subspace estimates
Journal of Econometrics, 2004Co-Authors: Alessandro Chiuso, Giorgio PicciAbstract:Abstract We give new simple general expressions for the Asymptotic coVariance of the estimated system parameters (A,B,C,D) in subspace identification. The formulas can be applied to a whole class of subspace methods including N4SID, MOESP, CVA, etc. The Asymptotic expressions highlight how the conditioning of the estimation problem influences the accuracy of the estimates.
B G Quinn - One of the best experts on this subject based on the ideXlab platform.
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estimation of frequency amplitude and phase from the dft of a time series
IEEE Transactions on Signal Processing, 1997Co-Authors: B G QuinnAbstract:In a previous paper, a frequency estimator using only three Fourier coefficients was introduced, which has Asymptotic Variance of order T/sup -3/. In this correspondence, a similar technique of Rife and Vincent (1970) is shown to have Asymptotic Variance of larger order. A new estimator is introduced that has Asymptotic Variance Less than 1.65 times the CRLB.
