Generalized Linear Models on Test Science Research Document Library https://research.testscience.org/keywords/generalized-linear-models/ Recent content in Generalized Linear Models on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Sun, 01 Jan 2017 00:00:00 +0000 Power Approximations for Generalized Linear Models using the Signal-to-Noise Transformation Method https://research.testscience.org/post/2017-power-approximations-for-generalized-linear-models-using-the-signal-to-noise-transformation-method/ Sun, 01 Jan 2017 00:00:00 +0000 https://research.testscience.org/post/2017-power-approximations-for-generalized-linear-models-using-the-signal-to-noise-transformation-method/ Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower. Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower. This paper details the process for defining an effect size, constructing thecoefficients for the test, and calculating power for the family of generalized linearmodels. The focus is on experimental designs that have multi-level categorical factors. A simulation study is performed which demonstrates that SNRx power results agreewith simulation.

Suggested Citation

Johnson, Thomas H., Laura Freeman, Jim Simpson, and Colin Anderson. “Power Approximations for Generalized Linear Models Using the Signal-to-Noise Transformation Method.” Quality Engineering 30, no. 3 (July 3, 2018): 511–24. https://doi.org/10.1080/08982112.2017.1361537.

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]]> Choice of Second-Order Response Surface Designs for Logistic and Poisson Regression Models https://research.testscience.org/post/2009-choice-of-second-order-response-surface-designs-for-logistic-and-poisson-regression-models/ Thu, 01 Jan 2009 00:00:00 +0000 https://research.testscience.org/post/2009-choice-of-second-order-response-surface-designs-for-logistic-and-poisson-regression-models/ This paper illustrates the construction of D-optimal second order designs for situations when the response is either binomial (pass/fail) or Poisson (count data). Suggested Citation Johnson, Rachel T., and Douglas C. Montgomery. “Choice of Second-Order Response Surface Designs for Logistic and Poisson Regression Models.” International Journal of Experimental Design and Process Optimisation 1, no. 1 (2009): 2. https://doi.org/10.1504/IJEDPO.2009.028954. Paper: This paper illustrates the construction of D-optimal second order designs for situations when the response is either binomial (pass/fail) or Poisson (count data).

Suggested Citation

Johnson, Rachel T., and Douglas C. Montgomery. “Choice of Second-Order Response Surface Designs for Logistic and Poisson Regression Models.” International Journal of Experimental Design and Process Optimisation 1, no. 1 (2009): 2. https://doi.org/10.1504/IJEDPO.2009.028954.

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