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Testing order restrictions in contingency tables

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  • R. Colombi
  • A. Forcina

Abstract

Though several interesting models for contingency tables are defined by a system of inequality constraints on a suitable set of marginal log-linear parameters, the specific features of the corresponding testing problems and the related procedures are not widely well known. After reviewing the most common difficulties which are intrinsic to inequality restricted testing problems, the paper concentrates on the problem of testing a set of equalities against the hypothesis that these are violated in the positive direction and also on testing the corresponding inequalities against the saturated model; we argue that valid procedures should consider these two testing problems simultaneously. By reformulating and adapting procedures appeared in the econometric literature, we propose a likelihood ratio and a multiple comparison procedure which are both based on the joint distribution of two relevant statistics; these statistics are used to divide the sample space into three regions: acceptance of the assumed equality constraints, rejection towards inequalities in the positive direction and rejection towards the unrestricted model. A simulation study indicates that the likelihood ratio based procedure perform substantially better. Our procedures are applied to the analysis of two real data sets to clarify how they work in practice. Copyright Springer-Verlag Berlin Heidelberg 2016

Suggested Citation

  • R. Colombi & A. Forcina, 2016. "Testing order restrictions in contingency tables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 73-90, January.
    • Handle: RePEc:spr:metrik:v:79:y:2016:i:1:p:73-90
    DOI: 10.1007/s00184-015-0544-x
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    References listed on IDEAS

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    1. Valentino Dardanoni & Antonio Forcina, 1999. "Inference for Lorenz curve orderings," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 49-75.
    2. Valentino Dardanoni & Mario Fiorini & Antonio Forcina, 2012. "Stochastic monotonicity in intergenerational mobility tables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 85-107, January.
    3. Bartolucci F. & Forcina A. & Dardanoni V., 2001. "Positive Quadrant Dependence and Marginal Modeling in Two-Way Tables With Ordered Margins," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1497-1505, December.
    4. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    5. Bishop, John A & Formby, John P & Smith, W James, 1991. "Lorenz Dominance and Welfare: Changes in the U.S. Distribution of Income, 1967-1986," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 134-139, February.
    6. Manuela Cazzaro & Roberto Colombi, 2006. "Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 177-190, August.
    7. Colombi, Roberto & Giordano, Sabrina & Cazzaro, Manuela, 2014. "hmmm: An R Package for Hierarchical Multinomial Marginal Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i11).
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