• 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 Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.31-37
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• 1989

The recursive residuals are obtained by the iterative processes as descrbed in section 2. They may require more efforts and time to compute and may face difficultie in ordering of data. But we can investigate each case to be deleted and gather more informations on each case. The recursive residuals are much more effective with conjecture of cusum technique. We suggest to use the predicted residual for the construction of recursive residuals in nonlinear regression models. The assessment of influence and leverage by the connection with recursive residuals will be necessary.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.

• Journal of Applied Biological Chemistry
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• v.60 no.3
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• pp.249-256
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• 2017

The Monascus azaphilone (MAz) pigment is a well-known food colorant that has yellow, orange and red components. The structures of the yellow and orange MAz differ by two hydride reductions, with yellow MAz being the reduced form. Orange MAz can be non-enzymatically converted to red MAz in the presence of amine derivatives. It was previously demonstrated that mppE and mppG are involved in the biosynthesis of yellow and orange MAz, respectively. However, ${\Delta}mppE$ and ${\Delta}mppG$ knockout mutants maintained residual production of yellow and orange MAz, respectively. In this study, we deleted the region encompassing mppD, mppE and mppG in M. purpureus and compared the phenotype of the resulting mutant (${\Delta}mppDEG$) with that of an mppD knockout mutant (${\Delta}mppD$). It was previously reported that the ${\Delta}mppD$ strain retained the ability to produce MAz but at approximately 10% of the level observed in the wildtype strain. A chemical analysis demonstrated that the ${\Delta}mppDEG$ strain was still capable of producing both yellow and orange MAz, suggesting the presence of minor MAz route(s) not involving mppE or mppG. Unexpectedly, the ${\Delta}mppDEG$ strain was observed to accumulate fast-eluting pigments in a reverse phase high-performance liquid chromatography analysis. A LC-MS analysis identified these pigments as ethanolamine derivatives of red MAz, which had been previously identified in an mppE knockout mutant that produces high amounts of orange MAz. Although the underlying mechanism is largely unknown, this study has yielded an M. purpureus strain that selectively accumulates red MAz.