Response modeling methodology

Shore, Haim

doi:10.1002/wics.151

Cited by 7 publications

(8 citation statements)

References 26 publications

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“…A plot similar to that in Fig. 1 is obtained if Z is non normal but symmetrically distributed, for example, the standard logistic variable (refer for some examples to Shore, 2005Shore, , 2011, and references therein).…”

Section: Realization Of the New Paradigm For A Set Of 27 Distributionmentioning

confidence: 60%

“…Recently, a new approach for modeling monotone convex relations had been introduced, denoted response modeling methodology (RMM; Shore, 2005Shore, , 2011Shore, , 2012. A unique property of RMM is its CMC property.…”

Section: Assertion [3]mentioning

confidence: 99%

“…From (A.8) therein we have an RMM model for Q Y (z;θ ), as currently used to model random variation (refer to Shore, 2010Shore, , 2011Shore, , 2012, and references therein):…”

Section: Traditional Modeling Of Random Variationmentioning

confidence: 99%

“…Note that for (7) to represent the Z-based quantile function of a proper distribution function we need to have: a > 0, c > 0 . Examples of (6) and (7) fitted to existing distributions or to sample data are given in Shore (2005Shore ( , 2007Shore ( , 2010Shore ( , 2011, Shore and A'wad (2010), and Shauly and Parmet (2011). Examples for using (6) and (7) to replace (approximate) published models of monotone convex relationships may be found in Shore (2014), Benson-Karhi et al (2014).…”

Section: Traditional Modeling Of Random Variationmentioning

confidence: 99%

“…The quantile function associated with (A.1) is (Shore, 2005(Shore, , 2011 Equating the quantiles of (7) (with L = 0) to the above (exact) Y quantiles, the following expressions are obtained for the parameters {a,b,c} as function of these quantiles and m (the latter replacing q50 for reasons expounded below):…”

Section: Appendix A: Brief Tutorial On Rmmmentioning

confidence: 99%

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A General Model of Random Variation

Shore

2014

Communications in Statistics - Theory and Methods

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A statistical distribution of a random variable is uniquely represented by its normalbased quantile function. For a symmetrical distribution it is S-shaped (for negative kurtosis) and inverted S-shaped (otherwise). As skewness departs from zero, the quantile function gradually transforms into a monotone convex function (positive skewness) or concave function (otherwise). Recently, a new general modeling platform has beenintroduced, response modeling methodology, which delivers good representation to monotone convex relationships due to its unique "continuous monotone convexity" property. In this article, this property is exploited to model the normal-based quantile function, and explored using a set of 27 distributions.

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Section: Realization Of the New Paradigm For A Set Of 27 Distributionmentioning

confidence: 60%

Section: Assertion [3]mentioning

confidence: 99%

“…From (A.8) therein we have an RMM model for Q Y (z;θ ), as currently used to model random variation (refer to Shore, 2010Shore, , 2011Shore, , 2012, and references therein):…”

Section: Traditional Modeling Of Random Variationmentioning

confidence: 99%

Section: Traditional Modeling Of Random Variationmentioning

confidence: 99%

Section: Appendix A: Brief Tutorial On Rmmmentioning

confidence: 99%

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A General Model of Random Variation

Shore

2014

Communications in Statistics - Theory and Methods

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The Effects of the Box–Cox Transformation

Shore

2023

Wiley StatsRef: Statistics Reference Online

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The expected effects of the Box–Cox transformation (BCT), when applied to available data, have been summarized in the original article, where the transformation was first introduced. It was to achieve validity for the three major assumptions made in the analysis of linear models, that is, simplicity of structure (of the linear model), constancy of error variance, and normality of distributions. While many articles have been published to date about estimating the parameter(s) of BCT, or suggesting alternatives, little attention has been given to the more fundamental question – Why should a data power transformation be able to achieve these objectives (as cumulative empirical evidence obviously attest to)? In this article, we address this question and attempt clear explanation with regard to each of the objectives that BCT attempts to achieve.

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Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Shore

2013

Quality & Reliability Eng

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Statistical process control monitoring of nonlinear relationships (profiles) has been the subject of much research recently. While attention is primarily given to the statistical aspects of the monitoring techniques, little effort has been devoted to developing a general modeling approach that would introduce ‘uniformity of practice’ in modeling nonlinear profiles (analogously with the three‐sigma limits of Shewhart control charts). In this article, we use response modeling methodology (RMM) to demonstrate implementation of this approach to statistical process control monitoring of ecological relationships. Using 10 ecological models that have appeared in the literature, it is first shown that RMM models can replace (approximate) current ecological models with negligible loss in accuracy. Computer simulation is then used to demonstrate that estimated RMM models and estimated data generating ecological models achieve goodness‐of‐fit that is practically indistinguishable from one another. A regression‐adjusted control scheme, based on control charts for the predicted median and for residuals variation, is developed and demonstrated for three types of ‘out of control’ scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

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Response modeling methodology

Cited by 7 publications

References 26 publications

A General Model of Random Variation

A General Model of Random Variation

The Effects of the Box–Cox Transformation

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

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