STATISTICAL INFERENCE FOR STATISTICAL TABLES WITH NONDISCLOSURE CELLS

In this paper, we discuss an analysis for statistical tables with nondisclosure cells when the data are presented in tabular form. Inaba and Iwasaki (1996, 1997) proposed procedures with the aim of imputing the values for nondisclosure cells. On the other hand, the major objective of this paper is to apply the methodology for estimation and hypothesis testing to the statistical tables with nondisclosure cells. The problem of statistical tables with nondisclosure cells is one type of incomplete data problems. Then, we use the nonignorable pattern-mixture model (Little (1993a); Rubin (1987)). The pattern-mixture model requires prior information to identify the parameters of the model concerning missing data. We propose a procedure for estimation and hypothesis testing, which does not relate the distribution of nondisclosure cells to the distribution of disclosure cells. From this point of view, proposed procedure is different from any procedures based on common nonignorable pattern-mixture model, which use the model assumed for missing data mechanism. Computations for estimation and hypothesis testing are straightforward by using a direct Monte Carlo simulation method. In terms of this method, sensitivity to model assumptions can be easily assessed.