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Cluster Analysis for Regime Identification in Stochastic Simulation Metamodeling

https://doi.org/10.2139/ssrn.4884919
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A maximum likelihood methodology for clusterwise linear regression
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Correction to algorithm 39 clusterwise linear regression
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Clusterwise linear least absolute deviations regression
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Algorithm 39 Clusterwise linear regression
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A New Approach to Estimating Switching Regressions
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Estimating Mixtures of Normal Distributions and Switching Regressions
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Maximum Likelihood Estimates of the Parameters of a Mixture of Two Regression Lines
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Proceedings of the 2004 Winter Simulation Conference
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Reinsch's smoothing spline simulation metamodels
inferredcites →https://doi.org/10.1109/wsc.2012.6464991openalex-extracted
Metamodeling of simulations consisting of time series inputs and outputs
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Using discrete event simulation to evaluate time series forecasting methods for security applications
inferredcites →https://doi.org/10.1177/0037549709102763openalex-extracted
Construction of Stochastic Simulation Metamodels based on Unreplicated Smoothed Data
inferredcites →https://doi.org/10.1177/00375497211018734openalex-extracted
Construction of stochastic simulation metamodels with segmented polynomials
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A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation”
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The Analysis of Simulation-Generated Time Series
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Tests of Equality Between Sets of Coefficients in Two Linear Regressions
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Good Classification Measures and How to Find Them
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An Exploratory Study of K-Means and Expectation Maximization Algorithms
inferredcites →https://doi.org/10.2139/ssrn.2627131openalex-snapshot
Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review
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Time Series Analysis: Forecasting and Control
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