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404,308
The Limit of FiniteSample Size and a Problem with Subsampling
, 2007
"... This paper considers inference based on a test statistic that has a limit distribution that is discontinuous in a nuisance parameter or the parameter of interest. The paper shows that subsample, bn <nbootstrap, and standard fixed critical value tests based on such a test statistic often have asym ..."
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Cited by 12 (3 self)
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asymptotic size–defined as the limit of the finitesample size–that is greater than the nominal level of the tests. We determine precisely the asymptotic size of such tests under a general set of highlevel conditions that are relatively easy to verify. The highlevel conditions are verified in several
Grouped Model Averaging for Finite Sample Size
"... Abstract: This paper studies grouped model averaging methods for finite sample size situation. Sufficient conditions under which the grouped model averaging estimator dominates the ordinary least squares estimator are provided. A class of grouped model averaging estimators, gclass, is introduced, ..."
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Abstract: This paper studies grouped model averaging methods for finite sample size situation. Sufficient conditions under which the grouped model averaging estimator dominates the ordinary least squares estimator are provided. A class of grouped model averaging estimators, gclass, is introduced
Finite Sample Size Optimality of GLR Tests
, 903
"... In binary hypothesis testing, when the hypotheses are composite or the corresponding data pdfs contain unknown parameters, one can use the well known generalized likelihood ratio test (GLRT) to reach a decision. This test has the very desirable characteristic of performing simultaneous detection and ..."
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Cited by 2 (0 self)
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, finite sample size, detection/estimation formulation for the problem of hypothesis testing with unknown parameters and a corresponding detection/isolation setup for the case of composite hypotheses, is introduced. The resulting optimum scheme has a GLRTlike form which is closely related to the criterion
Quantum state discrimination bounds for finite sample size
 Journal of Mathematical Physics, Volume 53, Nmumber
, 2012
"... In the problem of quantum state discrimination, one has to determine by measurements the state of a quantum system, based on the a priori side information that the true state is one of two given and completely known states, ρ or σ. In general, it is not possible to decide the identity of the true s ..."
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Cited by 8 (3 self)
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complete solution to the asymptotic problem, they are not completely satisfying from a practical point of view. Indeed, in realistic scenarios one has access only to finitely many copies of a system, and therefore it is desirable to have bounds on the error probabilities for finite sample size
1 Quantum state tomography with finite sample size ∗
"... The estimation of the density matrix of a klevel quantum system is studied when the parametrization is given by the real and imaginary part of the entries and they are estimated by a finite number of independent measurements. For pure or nearly pure states, a simple and provable correct constrainin ..."
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The estimation of the density matrix of a klevel quantum system is studied when the parametrization is given by the real and imaginary part of the entries and they are estimated by a finite number of independent measurements. For pure or nearly pure states, a simple and provable correct
Finite Sample Size Results for Robust Model Selection; Application to Neural Networks
 Technical Report, Dept. E.E., Technion, Publication # CC117
, 1995
"... The problem of model selection in the face of finite sample size is considered within the framework of statistical decision theory. Focusing on the special case of regression, we introduce a model selection criterion which is shown to be robust in the sense that, with high confidence, even for a fin ..."
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Cited by 2 (1 self)
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The problem of model selection in the face of finite sample size is considered within the framework of statistical decision theory. Focusing on the special case of regression, we introduce a model selection criterion which is shown to be robust in the sense that, with high confidence, even for a
An approximation to the distribution of finite sample size mutual information estimates
 ICC
, 2004
"... Abstract — In this paper, the distribution of mutual information between two discrete random variables is approximated by means of a secondorder Taylor series expansion. Approximative expressions for the distribution of mutual information (MI) between independent random variables, conditional MI be ..."
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Cited by 15 (1 self)
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between conditionally independent variables, and MI between (weakly) dependent random variables are derived. These distributions are functions of the available sample size and the number of realisations of the random variables only; knowledge of the variables ’ PMF is not required. The results
The limit of finite sample size and a problem with subsampling. Cowles Foundation discussion paper
, 2005
"... This paper considers tests and confidence intervals based on a test statistic that has a limit distribution that is discontinuous in a nuisance parameter or the parameter of interest. The paper shows that standard fixed critical value (FCV) tests and subsample tests often have asymptotic size–define ..."
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Cited by 10 (1 self)
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–defined as the limit of the finite sample size–that is greater than the nominal level of the test. We determine precisely the asymptotic size of such tests under a general set of highlevel conditions that are relatively easy to verify. Often the asymptotic size is determined by a sequence of parameter values
NearOptimal Unit Root Tests with Stationary Covariates with Better Finite Sample Size
, 2006
"... Abstract. Numerous tests for integration and cointegration have been proposed in the literature. Since Elliott, Rothemberg and Stock (1996) the search for tests with better power has moved in the direction of finding tests with some optimality properties both in univariate and multivariate models. A ..."
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Cited by 1 (0 self)
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. Although the optimal tests constructed so far have asymptotic power that is indistinguishable from the power envelope, it is well known that they can have severe size distortions in finite samples. This paper proposes a simple and powerful test that can be used to test for unit root
Empirical bias results for the datadriven HaarFisz transform for finite sample sizes
, 2007
"... The datadriven HaarFisz (DDHF) transformation was recently developed to stabilise the variance of data with an increasing (but otherwise unknown) meanvariance relationship. This report investigates the empirical bias of the DDHF transform and compares it to the much used BoxCox transformation, u ..."
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Cited by 1 (1 self)
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The datadriven HaarFisz (DDHF) transformation was recently developed to stabilise the variance of data with an increasing (but otherwise unknown) meanvariance relationship. This report investigates the empirical bias of the DDHF transform and compares it to the much used BoxCox transformation, using both simulated Poisson counts and data of the deaths of coalition personnel in Iraq. 1
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