Results 301 to 310 of about 1,171,948 (354)

Generating confidence intervals by Monte Carlo simulations

2004
Abstract As discussed in the previous chapter, the asymptotic method built in to most nonlinear regression programs is only an approximate method to determine the confidence interval of a best-fit parameter. This chapter presents one alternative method, and the next chapter presents still another alternative method.
Harvey Motulsky, Arthur Christopoulos
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On Generating Confidence Intervals for Nonparametric Methods

2017
Nonparametric techniques provide no analytical solutions for confidence intervals. The bootstrap and jackknife methods are applied to Data Envelopment Analysis to generate different confidence intervals, which affect the probability of committing a type I error.
Wildman, John, Hollingsworth, Bruce
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Bootstrap generated confidence interval for time averaged measure

International Journal of Modeling, Simulation, and Scientific Computing, 2015
In the simulation output analysis, there are some measures that should be calculated by time average concept such as the mean queue length. Especially, the confidence interval of those measures might be required for statistical analysis. In this situation, the traditional method that utilizes the central limit theorem (CLT) is inapplicable if the ...
Jinsoo Park, Haneul Lee, Yun Bae Kim
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Confidence intervals in generalized method of moments models

Journal of Econometrics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Imbens, Guido W., Spady, Richard
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A General Approach to Ridge Analysis with Confidence Intervals

Technometrics, 1993
In response-surface methodology, ridge analysis is a graphical technique for interpreting response surfaces, particularly those of three or more dimensions. Standard ridge-analysis techniques employ a second-order polynomial regression model. An approach to ridge analysis is presented here that generalizes its current scope.
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Exact confidence intervals generated by conditional parametric bootstrapping

Journal of Applied Statistics, 1999
Conditional parametric bootstrapping is defined as the samples obtained by performing the simulations in such a way that the estimator is kept constant and equal to the estimate obtained from the data. Order statistics of the bootstrap replicates of the parameter chosen in each simulation provide exact confidence intervals, in a probabilistic sense, in
Magnar Lillegard, Steinar Engen
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Expanding Reliability Generalization: Confidence Intervals and Charter's Combined Reliability Coefficient

Perceptual and Motor Skills, 2004
Methods of reliability generalization characterize the reliability of a scale's scores across studies. Charter in 2003 presented useful formulae for computing combined estimates of reliability. The present article illustrates use of Charter's formulae and application of confidence intervals for reliability coefficients, including the combined estimate.
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A General Method for Constructing Simultaneous Confidence Intervals

Journal of the American Statistical Association, 1982
Abstract A general method is proposed for constructing simultaneous confidence intervals that gives special emphasis to any preselected finite spanning subset in a linear space of estimable functions. The method has a wide range of application, including regression analysis.
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Assessment of Two Process Capabilities by Using Generalized Confidence Intervals and its Applications

Annals of Data Science, 2022
Mahendra Saha
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Generating Monte Carlo Confidence Intervals by the Robbins-Monro Process

Applied Statistics, 1992
Summary: A new use of the Robbins-Monro search process to generate Monte Carlo confidence intervals for a single-parameter density function is described. When the optimal value of a 'step length constant' is known, asymptotically the process gives exact confidence intervals and is fully efficient.
Garthwaite, P. H., Buckland, S. T.
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