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On Confidence Intervals in Nonparametric Binary Regression via Edgeworth Expansions

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  • Rodriguez-Campos, M. Celia

Abstract

Local confidence intervals for regression function with binary response variable are constructed. These intervals are based on both theoretical and "plug-in" normal asymptotic distribution of a usual statistic. In the plug-in approach, two ways of estimating bias are proposed; for them we obtain the mean squared error and deduce an expression of an optimal bandwidth. The rate of convergence of theoretical distributions to their limits is obtained by means of Edgeworth expansions. Likewise, these expansions allow us to deduce properties about the coverage probability of the confidence intervals. Theoretic approximations to that probability are compared in a simulation study with the corresponding coverage rates.

Suggested Citation

  • Rodriguez-Campos, M. Celia, 1999. "On Confidence Intervals in Nonparametric Binary Regression via Edgeworth Expansions," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 218-241, May.
  • Handle: RePEc:eee:jmvana:v:69:y:1999:i:2:p:218-241
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    References listed on IDEAS

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    1. Babu, Gutti Jogesh, 1991. "Edgeworth expansions for statistics which are functions of lattice and non-lattice variables," Statistics & Probability Letters, Elsevier, vol. 12(1), pages 1-7, July.
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    Cited by:

    1. Geenens, Gery & Simar, LĂ©opold, 2010. "Nonparametric tests for conditional independence in two-way contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 765-788, April.

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