Search results for "UNBI"

showing 10 items of 31 documents

Finite Sample Efficiency and Drawbacks: An Illustration

2011

Historically, finite-sample efficiency was the first notion of optimality introduced and it is still encountered in introductory statistics texts. The definition has several drawbacks however, one being that it is restricted to the class of unbiased estimators. An example is given to illustrate this.

Mathematical optimizationEfficiencyStein's unbiased risk estimateEstimatorSample (statistics)Class (philosophy)U-statisticMathematicsSSRN Electronic Journal
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Unbiased Branches: An Open Problem

2007

The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 in…

Mathematical optimizationMarkov chainComputer sciencebusiness.industryOpen problemPrediction by partial matchingBest linear unbiased predictionMachine learningcomputer.software_genreBranch predictorBenchmark (computing)Range (statistics)Artificial intelligenceHardware_CONTROLSTRUCTURESANDMICROPROGRAMMINGbusinesscomputerInteger (computer science)
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Effective state estimation of stochastic systems

2003

In the present paper, for constructing the minimum risk estimators of state of stochastic systems, a new technique of invariant embedding of sample statistics in a loss function is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant estimator, which has smaller risk than any of the well‐known estimators. There exists a class of control systems where observations are not …

Mathematical optimizationMinimum mean square errorMathematical statisticsEstimatorTheoretical Computer ScienceMinimum-variance unbiased estimatorEfficient estimatorBias of an estimatorControl and Systems EngineeringPrior probabilityComputer Science (miscellaneous)Applied mathematicsEngineering (miscellaneous)Social Sciences (miscellaneous)Invariant estimatorMathematicsKybernetes
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Constrained minimum variance control of nonsquare LTI MIMO systems

2010

Constrained minimum variance control is offered for nonsquare LTI MIMO systems. A constrained control design takes advantage of the so-called control zeros. The new control strategy is compared with familiar generalized minimum variance control and possible application areas of the two are discussed.

Mathematical optimizationMinimum-variance unbiased estimatorApplication areasbusiness.industryRobustness (computer science)Linear systemMIMOPole–zero plotbusinessAutomationMimo systemsMathematics2010 15th International Conference on Methods and Models in Automation and Robotics
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Construction and optimality of a special class of balanced designs

2006

The use of balanced designs is generally advisable in experimental practice. In technological experiments, balanced designs optimize the exploitation of experimental resources, whereas in marketing research experiments they avoid erroneous conclusions caused by the misinterpretation of interviewed customers. In general, the balancing property assures the minimum variance of first-order effect estimates. In this work the authors consider situations in which all factors are categorical and minimum run size is required. In a symmetrical case, it is often possible to find an economical balanced design by means of algebraic methods. Conversely, in an asymmetrical case algebraic methods lead to e…

Mathematical optimizationOrthogonality (programming)Computer scienceHeuristic (computer science)Property (programming)Settore SECS-S/02 - Statistica Per La Ricerca Sperimentale E TecnologicaManagement Science and Operations Researchbalancingnearly orthogonalarraytwo- and three-level designsoptimalityEmpirical researchMinimum-variance unbiased estimatorEconometricsinteraction estimabilityAlgebraic numberSafety Risk Reliability and QualityMarketing researchCategorical variableasymmetrical (mixed-level) design
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Signal Restoration via a Splitting Approach

2012

International audience; In the present study, a novel signal restoration method from noisy data samples is presented and is termed as "signal split (SSplit)" approach. The new method utilizes Stein unbiased risk estimate estimator to split the signal, the Lipschitz exponents to identify noise elements and a heuristic approach for the signal reconstruction. However, unlike many noise removal techniques, the present method works only in the non-orthogonal domain. Signal restoration was performed on each individual part by finding the best compromise between the data samples and the smoothing criteria. Statistical results are quite promising and suggest better performance than the conventional…

Mathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingsplit or segmentationthresholding02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingSignalmodulus maxima[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringLipschitz exponentMathematicscontinuous wavelet transformSignal reconstructionHeuristicNoise (signal processing)Estimator020206 networking & telecommunicationsLipschitz continuityStein unbiased risk estimatewavelet transform modulus maxima020201 artificial intelligence & image processingAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSmoothingEnergy (signal processing)
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Comparing Correlation Matrix Estimators Via Kullback-Leibler Divergence

2011

We use a self-averaging measure called Kullback-Leibler divergence to evaluate the performance of four different correlation estimators: Fourier, Pearson, Maximum Likelihood and Hayashi-Yoshida estimator. The study uses simulated transaction prices for a large number of stocks and different data generating mechanisms, including synchronous and non-synchronous transactions, homogeneous and heterogeneous inter-transaction time. Different distributions of stock returns, i.e. multivariate Normal and multivariate Student's t-distribution, are also considered. We show that Fourier and Pearson estimators are equivalent proxies of the `true' correlation matrix within all the settings under analysis…

Minimum-variance unbiased estimatorEfficient estimatorKullback–Leibler divergenceConsistent estimatorStatisticsEstimatorMultivariate normal distributionTrimmed estimatorInvariant estimatorMathematicsSSRN Electronic Journal
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A kriging interpolation strategy for the optimization of Acidithiobacillus ferrooxidans biomass production using fed-batch bioreactors

2008

In this work, a procedure for the optimization of Acidithiobacillus ferrooxidans biomass production in fed-batch reactors using a model based on optimal spatial interpolation of experimental data is proposed. The approach is useful in those cases where specific growth and substrate consumption rates are unknown. Based on interpolation, the optimal values of biomass and substrate concentrations set points are obtained at the minimum of 2-dimensional cost function. In the fed-batch reactor biomass and substrate concentrations are controlled at their set points by changing the input flow and its concentration. We propose a minimum variance control strategy which improves the classical proporti…

Nonlinear systemMinimum-variance unbiased estimatorChemistryKrigingMaterials ChemistryMetals and AlloysBiomassFunction (mathematics)Biological systemUpper and lower boundsIndustrial and Manufacturing EngineeringInterpolationMultivariate interpolationHydrometallurgy
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Limits on entropic uncertainty relations

2010

We consider entropic uncertainty relations for outcomes of the measurements of a quantum state in 3 or more mutually unbiased bases (MUBs), chosen from the standard construction of MUBs in prime dimension. We show that, for any choice of 3 MUBs and at least one choice of a larger number of MUBs, the best possible entropic uncertainty relation can be only marginally better than the one that trivially follows from the relation by Maassen and Uffink for 2 bases.

Nuclear and High Energy PhysicsGeneral Physics and AstronomyStatistical and Nonlinear PhysicsState (functional analysis)Prime (order theory)Theoretical Computer ScienceCombinatoricsComputational Theory and MathematicsDimension (vector space)Entropic uncertaintyNuclear ExperimentMathematical PhysicsMutually unbiased basesMathematicsQuantum Information and Computation
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Wavelet-like orthonormal bases for the lowest Landau level

1994

As a first step in the description of a two-dimensional electron gas in a magnetic field, such as encountered in the fractional quantum Hall effect, we discuss a general procedure for constructing an orthonormal basis for the lowest Landau level, starting from an arbitrary orthonormal basis in L2(R). We discuss in detail two relevant examples coming from wavelet analysis, the Haar and the Littlewood-Paley bases.

PhysicsMathematics::Functional AnalysisGeneral Physics and AstronomyStatistical and Nonlinear PhysicsLandau quantizationMagnetic fieldGeneralized Fourier seriesWaveletFractional quantum Hall effectOrthonormal basisQuantum field theorySettore MAT/07 - Fisica MatematicaMutually unbiased basesMathematical PhysicsMathematical physics
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