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90,442
On the finite sample performance of the nearest neighbor classifier
 IEEE Transactions on Information Theory
, 1994
"... AbstructThe finite sample performance of a nearest neighbor classifier is analyzed for a twoclass pattern recognition problem. An exact integral expression is derived for the msample risk R, given that a reference msample of labeled points is available to the classifier. The statistical setup as ..."
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Cited by 18 (2 self)
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AbstructThe finite sample performance of a nearest neighbor classifier is analyzed for a twoclass pattern recognition problem. An exact integral expression is derived for the msample risk R, given that a reference msample of labeled points is available to the classifier. The statistical setup
Finite sample performance of robust Bayesian regression
, 1995
"... This paper compares the finite sample performance of a particular Bayesian approach to robustly estimating a regression function, either linearly or nonparametrically, with several nonBayesian estimators. The comparison is based on simulation. The Bayesian approach models the errors as a mixture 2 ..."
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Cited by 2 (2 self)
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This paper compares the finite sample performance of a particular Bayesian approach to robustly estimating a regression function, either linearly or nonparametrically, with several nonBayesian estimators. The comparison is based on simulation. The Bayesian approach models the errors as a mixture
Finite sample performance of small versus large scale . . .
 BENJAMIN J., HEYMANS C., SEMBOLONI E., VAN WAERBEKE L., ET AL. 2007, ARXIV ASTROPHYSICS EPRINTS BINNEY J., TREMAINE S
, 1987
"... ..."
Finite sample performance of sequential designs for model identification
"... Abstract Classical regression analysis is usually performed in two steps. In a first step an appropriate model is identified to describe the data generating process and in a second step statistical inference is performed in the identified model. An intuitively appealing approach to the design of ex ..."
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of experiment for these different purposes are sequential strategies, which use parts of the sample for model identification and adapt the design according to the outcome of the identification steps. In this paper we investigate the finite sample properties of two sequential design strategies, which were
Multisensor Fusion Under Unknown Distributions  FiniteSample Performance Guarantees
 in Multisensor Fusion, A.K. Hyder (editor
, 2002
"... We consider a multiple sensor system such that for each sensor the outputs are related to the actual feature values according to a certain probability distribution. We present an overview of informational and computational aspects of a fuser that is required to combine the sensor outputs to more acc ..."
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Cited by 6 (5 self)
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accurately predict the feature, when the sensor distributions are unknown but iid measurements are given. Our focus is on methods to compute a fuser with probabilistic guarantees in terms of distributionfree performance bounds based on a finite sample. We first discuss a number of methods based
Assessing the Similarity of Distributions  Finite Sample Performance of the Empirical Mallows Distance
 J. Statist. Comput. Simul
, 1998
"... The problem of assessing similarity of two cumulative distribution functions (c.d.f.'s) has been the topic of a previous paper by the authors (Munk & Czado (1995)). Here, we developed an asymptotic test based on a trimmed version of the Mallows distance (Mallows 1972) between two c.d.f.&apo ..."
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Cited by 4 (2 self)
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.d.f.'s F and G. This allows to assess the similarity of two c.d.f.'s with respect to this distance at controlled type I error rate. In particular, this applies to bioequivalence testing within a purely nonparametric setting. In this paper, we investigate the finite sample behavior of this test
Finitesample performance guarantees for onedimensional stochastic root finding
 in Proceedings of the 2007 Winter Simulation Conference
"... We study the onedimensional root finding problem for increasing convex functions. We give gradientfree algorithms for both exact and inexact (stochastic) function evaluations. For the stochastic case, we supply a probabilistic convergence guarantee in the spirit of selectionofthebest methods. ..."
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Cited by 4 (2 self)
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. A worstcase bound on the work performed by the algorithm shows an improvement over naı̈ve stochastic bisection. 1
Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory
, 2006
"... Nonlinear functions of multivariate …nancial time series can exhibit long memory and fractional cointegration. However, tools for analysing these phenomena have principally been justi…ed under assumptions that are invalid in this setting. Determination of asymptotic theory under more plausible assum ..."
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Cited by 1 (0 self)
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assumptions can be complicated and lengthy. We discuss these issues and present a Monte Carlo study, showing that asymptotic theory should not necessarily be expected to provide a good approximation to …nitesample behaviour.
FINITESAMPLE PERFORMANCE ANALYSIS OF WIDELYLINEAR MULTIUSER RECEIVERS FOR DSCDMA SYSTEMS
"... In the framework of directsequence codedivision multipleaccess (DSCDMA) systems, finitesample performance results are established for two typical dataestimated implementations of the recently proposed widelylinear minimum outputenergy (WLMOE) multiuser receiver. Specifically, the signalt ..."
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Cited by 4 (2 self)
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In the framework of directsequence codedivision multipleaccess (DSCDMA) systems, finitesample performance results are established for two typical dataestimated implementations of the recently proposed widelylinear minimum outputenergy (WLMOE) multiuser receiver. Specifically, the signal
Results 1  10
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90,442