• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.687-698
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• 2000

An identification method is proposed in order to detect more than one outlying cells in multi-way contingency tables. The iterative proportional fitting method is applied to get expected values of several suspected outlying cells. Since the proposed method uses minimal sufficient statistics under quasi log-linear models, expected counts of outlying cells could be estimated under any hierarchical log-linear models. This method is an extension of the backwards-stepping method of Simonoff(1988) and requires les iteration to identify outlying cells.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.2
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• pp.157-160
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• 1999

We show the cumulants of a minimal sufficient statistics in k parameter natural exponential family by parameter function and partial parameter function. We nd the cumulants have some merits of central moments and general cumulants both. The first three cumulants are the central moments themselves and the fourth cumulant has the form related with kurtosis.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.45-64
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• 1995

When a random sample is taken from a certain class of discrete and continuous distributions whose support depend on two parameters, we could find that there exists the complete and sufficient statistic for parameters which belong to a certain class, and fomulate the uniformly minimum variance unbiased estimator (UMVUE) of any estimable function. Some UMVUE's of parametric functions are illustrated for the class of the distribution. Especially, we find that the UMVUE of some estimable parametric function from the truncated normal distribution could be expressed by the version of the Mill's ratio.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.887-894
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• 1998

Let T=( $T_1$,…, $T_{k}$;k$\geq$2) be a minimal sufficient and complete statistic for a k-parameter exponential model. Consider a partition of T into ( $T_1$, $T_2$), where $T_1$=( $T_1$,…, $T_{r}$ and $T_2$=( $T_{r+1}$,…, $T_{k}$1$\leq$r$\leq$k-1/). This article represents a way to obtain higher moments such as skewness and kurtosis for the distribution T and the conditional distribution of $T_1$, given $T_2$= $t_2$. These results are illustrated by some examples.s.les.s.