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Simreg: a Software Including Some New Developments in Multiple Comparison and Simultaneous Confidence Bands for Linear Regression Models

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  • Jamshidian, Mortaza
  • Liu, Wei
  • Zhang, Ying
  • Jamishidian, Farid

Abstract

The problem of simultaneous inference and multiple comparison for comparing means of k( ≥ 3) populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known. Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test.

Suggested Citation

  • Jamshidian, Mortaza & Liu, Wei & Zhang, Ying & Jamishidian, Farid, 2005. "Simreg: a Software Including Some New Developments in Multiple Comparison and Simultaneous Confidence Bands for Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i02).
    • Handle: RePEc:jss:jstsof:v:012:i02
    DOI: http://hdl.handle.net/10.18637/jss.v012.i02
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    References listed on IDEAS

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    1. Liu W. & Jamshidian M. & Zhang Y., 2004. "Multiple Comparison of Several Linear Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 395-403, January.
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    Cited by:

    1. Frank Schaarschmidt, 2017. "Multiple treatment comparisons in analysis of covariance with interaction," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 609-628, November.

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