For the analysis of the incomplete contingency tables with nonignorable nonresponse, the maximum likelihood estimates (ML) often give rise to estimates on the boundary solution of the parameter space and to imperfect fits for saturated log linear models. We present the explicit condition for the boundary solution arising from ML estimation for contingency tables with nonignorable nonresponse. To escape those conditions, we propose to use a power transformation as a link function instead of the usual logit link function, and show a power link function performed better than a logit link function in both ML estimation and Bayesian estimation under several scenarios of simulated data sets. We also demonstrate that a saturated nonignorable nonresponse model imperfectly fits the observation because the correlations of estimates are close to 1.
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Keywords: Boundary solutions problem; Generalized Linear Model; ML estimation; Bayesian estimation
Biography: Seongyong Kim, a Ph. D in Statistics since 2011, now works for Economics and Statistics Institute, Korea University Sejong Campus as a Research Professor.