• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.291-303
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• 2003

This work is proposed an alternative identification method of outlying cell which is one of important issues in categorical data analysis. One finds that there is a strong relationship between the location of an outlying cell and the corresponding parameter estimates of the well-fitted log-linear model. Among parameters of log-linear model, an outlying cell is affected by interaction terms rather than main effect terms. Hence one could identify an outlying cell by investigating of parameter estimates in an appropriate log-linear model.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.71-79
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• 1987

The linear and quadratic log contrast model with mixtures on the strictly positive simplex, $$ x_{q-1} = {(x_1, \cdots, x_q):\sum x_, = 1 and \delta \leq \frac{x_i}{x_j} \leq \frac{1}{\delta} for all i,j},$$ are considered. Using the invariance arguments, symmetric D-optimal designs are investigated. The class of symmetric D-optimal designs for the linear log contrasts model is given. Any D-optimal design for the quadratic log contrast model is shown to metric D-optimal designs for q=3 and 4 cases are given.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.519-527
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• 2004

Relationship between the partial likelihood ratio statistics for logisitic models and the partial goodness-of-fit statistics for corresponding log-linear models is discussed. This paper shows how definitions of suppression in logistic model can be adapted for log-linear model and how they are related to confounding in terms of collapsibility for categorical data. Several $2{times}2{times}2$ contingency tables are illustrated.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.131-139
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• 1993

Low cycle fatigue characteristics of cast aluminum alloy A356 with a yield strength and ultimate strength of 229 and 283 MPa respectively was evaluated using smooth axial specimen under strain controlled condition. Reversals to failure ranged from 16 to 107. The cast aluminum alloy exhibited cyclically strain-gardening behavior. The results of low cycle fatigue tests indicated that the conventional low cycle fatigue tests indicated that the conventional low cycle fatigue life model was not a satisfactory representation of the data. This occurred because the elastic strain-life curve was not-log-log linear and this phenomena caused a nonconservative and unsafe fatigue life prediction at both extremes of long and short lives. A linear log-log total strain-life model and a bilinear log-log elastic strain-life model were proposed in order to improve the representation of data compared to the conventional low cycle fatigue life model. Both proposed fatigue life models were statistically analyzed using F tests and successfully satisfied. However, the low cycle fatigue life model generated by the bilinear log-log elastic strain-life equation yielded a discontinuous curve with nonconservatism in the region of discontinuity. Among the models examined, the linear log-log total strain-life model provided the best representation of the low cycle fatigue data. Low cycle fatigue life prediction method based on the local strain approach could conveniently incorporated both proposed fatigue life models.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.755-764
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• 2006

Most graphical methods for categorical data can describe the structure of data and represent a measure of association among categorical variables. Among them the polyhedron plot represents sequential relationships among hierarchical log-linear models for a multidimensional contingency table. This kind of plot could be explored to describe the differences among sequential models. In this paper we suggest graphical methods, containing all the information, that reflect the relationship among all log-linear models in a certain hierarchical structure. We use the ideas of a correlation diagram.

• Journal of Environmental Science International
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• v.21 no.6
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• pp.695-704
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• 2012

The application of disinfection models on the plasma process was investigated. Nine empirical models were used to find an optimum model. The variation of parameters in model according to the operating conditions (first voltage, second voltage, air flow rate, pH) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Ralstonia Solanacearum which cause wilt in tomato plantation. Optimum disinfection models were chosen among the nine models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), $r^2$ values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum model was shown as Weibull+talil model followed by Log-linear+ Shoulder+Tail model. Two models were applied to the experimental data according to the variation of the operating conditions. In Weibull+talil model, Log10($N_o$), Log10($N_{res}$), ${\delta}$ and p values were examined. And in Log-linear+Shoulder+Tail model, the Log10($N_o$), Log10($N_{res}$), $k_{max}$, Sl values were calculated and examined.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.99-111
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• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.543-557
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• 2006

This paper is concerned with the applicability of the chi-square approximation to the six disparity statistics: the Pearson chi-square, the generalized likelihood ratio, the power divergence, the blended weight chi-square, the blended weight Hellinger distance, and the negative exponential disparity statistic. Three dimensional contingency tables of small and moderate sample sizes are generated to be fitted to all possible hierarchical log-linear models: the completely independent model, the conditionally independent model, the partial association models, and the model with one variable independent of the other two. For models with direct solutions of expected cell counts, point estimates and confidence intervals of the 90 and 95 percentage points of six statistics are explored. For model without direct solutions, the empirical significant levels and the empirical powers of six statistics to test the significance of the three factor interaction are computed and compared.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.357-366
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• 1999

Let us assume that two different log-linear models are selected by various model selection methods. When these are non-hierarchical it is not easy to choose one of these models. In this paper the well-known Cox's statistic is applied to compare these non-hierarchical log-linear models. Since it is impossible to obtain the analytic solution about the problem we proposed a alternative method by extending Pesaran and pesaran's (1993) simulation approach. We find that the values of proposed test statistic and the estimates are very much stable with some empirical results.