Journal of Data Science

Abstract: Through a series of carefully chosen illustrations from biometry and biomedicine, this note underscores the importance of using appropriate analytical techniques to increase power in statistical modeling and testing. These examples also serve to highlight some of the important recent devel opments in applied statistics of use to practitioner

Abstract: This note underscores important considerations that should be taken into account when teaching students to check for inadequacies of a given linear, nonlinear or logistic regression models. Key illustrations are provided which underscore the shortcomings of currently used procedures. A brief overview of nonlinear regression models is given in order to lay the foundation for testing for lack of fit in nonlinear models. This paper also introduces a new ’scaled’ binary logistic regression model to highlight po tential problems with the usual logistic model, and implications for choosing a robust optimal experimental design are also underscored and discussed. Key words: Lack of fit, logistic regression, nonlinear regression, optimal de

Abstract: This article presents and illustrates several important subset design approaches for Gaussian nonlinear regression models and for linear models where interest lies in a nonlinear function of the model parameters. These design strategies are particularly useful in situations where currentlyused subset design procedures fail to provide designs which can be used to fit the model function. Our original design technique is illustrated in conjuction with D-optimality, Bayesian D-optimality and Kiefer’s Φk-optimality, and is extended to yield subset designs which take account of curvature.