On Confidence Intervals in Nonparametric Binary Regression via Edgeworth Expansions
✍ Scribed by M.Celia Rodrı́guez-Campos
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 197 KB
- Volume
- 69
- Category
- Article
- ISSN
- 0047-259X
No coin nor oath required. For personal study only.
✦ Synopsis
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.