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A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification. [PDF]
BMC Med Res MethodolOzen H, Colak E, Ozen D.
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Batching Adaptive Variance Reduction
ACM Transactions on Modeling and Computer Simulation, 2023Adaptive Monte Carlo variance reduction is an effective framework for running a Monte Carlo simulation along with a parameter search algorithm for variance reduction, whereas an initialization step is required for preparing problem parameters in some instances.
Chenxiao Song, Reiichiro Kawai
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1988
In this chapter, we discuss various techniques which may be used to make calculations more efficient. In some cases, these techniques require that no further approximations be made to the transport physics. In other cases, the gains in computing speed come at the cost of computing results which may be less accurate since approximations are introduced ...
Bielajew, A. F., Rogers, D. W. O.
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In this chapter, we discuss various techniques which may be used to make calculations more efficient. In some cases, these techniques require that no further approximations be made to the transport physics. In other cases, the gains in computing speed come at the cost of computing results which may be less accurate since approximations are introduced ...
Bielajew, A. F., Rogers, D. W. O.
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Variance reduction and nonnormality
Biometrika, 1974There are many available variance reduction methods and these are described in the sampling theory literature in such works as Kish (1965) and Raj (1968), and in the literature of Monte Carlo methods (Hammersley & Handscomb, 1964) and in the survey paper by Halton (1970).
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Journal of the Operational Research Society, 1985
Estimating real-world parameter values by means of Monte-Carlo/stochastic simulation is usually accomplished by carrying out a number ‘n’ of computer runs, each using random numbers taken from a pseudo-random number generator. In order to improve the accuracy of the estimate (reduce the estimate's variance), the most common recourse is to increase n ...
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Estimating real-world parameter values by means of Monte-Carlo/stochastic simulation is usually accomplished by carrying out a number ‘n’ of computer runs, each using random numbers taken from a pseudo-random number generator. In order to improve the accuracy of the estimate (reduce the estimate's variance), the most common recourse is to increase n ...
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Variance Reduction in Smoothing Splines
Scandinavian Journal of Statistics, 2009Abstract. We develop a variance reduction method for smoothing splines. For a given point of estimation, we define a variance‐reduced spline estimate as a linear combination of classical spline estimates at three nearby points. We first develop a variance reduction method for spline estimators in univariate regression models.
Paige, Robert L., Sun, Shan, Wang, Keyi
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Water Resources Research, 1985
This paper presents an algorithm for optimal data collection in random fields, the so‐called variance reduction analysis, which is an extension of kriging. The basis of variance reduction analysis is an information response function (i.e., the amount of information gain at an arbitrary point due to a measurement at another site).
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This paper presents an algorithm for optimal data collection in random fields, the so‐called variance reduction analysis, which is an extension of kriging. The basis of variance reduction analysis is an information response function (i.e., the amount of information gain at an arbitrary point due to a measurement at another site).
openaire +1 more source