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Extended generalized linear models: Simultaneous estimation of flexible link and variance functions

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  • Anirban Basu

    (Section of General Internal Medicine, University of Chicago)

Abstract

I describe a command that simultaneously solves the extended es- timating equations estimator for parameters in the link and variance functions along with those of the linear predictor in a generalized linear model. The method addresses difficulties in choosing the correct link and variance functions in these models. It decouples the scale of estimation for the mean model, determined by the link function, from the scale of interest for the scientifically relevant effects. It also estimates a flexible variance structure from the data, leading to efficient estimation. Copyright 2005 by StataCorp LP.

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  • Anirban Basu, 2005. "Extended generalized linear models: Simultaneous estimation of flexible link and variance functions," Stata Journal, StataCorp LP, vol. 5(4), pages 501-516, December.
    • Handle: RePEc:tsj:stataj:v:5:y:2005:i:4:p:501-516
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    References listed on IDEAS

    as
    1. Joseph Hilbe, 1993. "Generalized linear models," Stata Technical Bulletin, StataCorp LP, vol. 2(11).
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
    4. Wooldridge, Jeffrey M, 1992. "Some Alternatives to the Box-Cox Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(4), pages 935-955, November.
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    Cited by:

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    2. Theodoros Mantopoulos & Paul M. Mitchell & Nicky J. Welton & Richard McManus & Lazaros Andronis, 2016. "Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 927-938, November.
    3. Linnea Polgreen & John Brooks, 2012. "Estimating Incremental Costs with Skew," Applied Health Economics and Health Policy, Springer, vol. 10(5), pages 319-329, September.
    4. Julie Shi, 2017. "Efficiency in Plan Choice with Risk Adjustment and Risk-Based Pricing in Health Insurance Exchanges," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 42(1), pages 79-113, January.
    5. Ian M. McCarthy, 2015. "Putting the Patient in Patient Reported Outcomes: A Robust Methodology for Health Outcomes Assessment," Health Economics, John Wiley & Sons, Ltd., vol. 24(12), pages 1588-1603, December.
    6. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.

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