• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.197-206
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• 2000

For estimating missing cells in contingency table, we suggest an iterative method which extends IPF (Iterative Proportional Fitting) method. The suggested m~thod is not restricted by the number and the location of missing cells, and does not distort the given quasi-independency.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.327-341
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• 2006

Winkler (1990, 2001), Sitter and Skinner (1994), Wilson and Sitter (2002) present a method which applies linear programing to designing surveys with multi-way stratification, primarily in situation where the desired sample size is less than or only slightly larger than the total number of stratification cells. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples, by evaluating sample mean, variance estimation, and mean squared errors, and by simulating sample mean for all methods. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. In this article their approach is applied to multi-way stratification using real data.