AI Article Synopsis
- The paper investigates inference in multi-response, nonlinear models involving multiple response variables and their interrelations.
- A system estimation method is proposed for simultaneous estimation and inference of model parameters, focusing on a bivariate Emax model applied to diabetes dose-response data.
- Results indicate that using system estimation enhances precision, especially when dependencies exist among the response variables, outperforming traditional equation-by-equation estimation methods.
Article Abstract
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
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Article Synopsis
- The paper investigates inference in multi-response, nonlinear models involving multiple response variables and their interrelations.
- A system estimation method is proposed for simultaneous estimation and inference of model parameters, focusing on a bivariate Emax model applied to diabetes dose-response data.
- Results indicate that using system estimation enhances precision, especially when dependencies exist among the response variables, outperforming traditional equation-by-equation estimation methods.
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