• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.43-50
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• 2008

Multinomial logistic regression is a well known multiclass classification method in the field of statistical learning. More recently, the development of sparse multinomial logistic regression model has found application in microarray classification, where explicit identification of the most informative observations is of value. In this paper, we propose a sparse multinomial kernel logistic regression model, in which the sparsity arises from the use of a Laplacian prior and a fast exact algorithm is derived by employing a bound optimization approach. Experimental results are then presented to indicate the performance of the proposed procedure.

• Journal of the Korean Statistical Society
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• v.13 no.1
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• pp.1-19
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• 1984

A random effects model of a one-way layout contingency table is developed using a Dirichlet-multinomial distribution. A test statistic, say $T_k$, is suggested for detecting Dirichlet-multinomial departure from a multinomial distribution. It is shown that the $T_k$ test is asymptotically superior to the classical chi-square test based on the asymptotic relative efficiency. This superiority is further evidenced by a Monte Carlo simulation.

• Journal of Korean Society of Transportation
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• v.13 no.4
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• pp.47-59
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• 1995

The main purpose of this study is identifying the factors which affect the mode choice behavior of work trips. Disaggregate behavioral approach is used for the analysis . The data were collected using the questionnaire survey method in Taegu. Two models were developed in this study which are multinomial logit model(MODEL-1) for auto, taxi and bus and multinomial logit model (MODEL-2) for auto, taxi, bus and subway. The stated preference (SP) data were used for the analysis of the subway mode choice behavior. MODEL-1 provide reasonable results for the future application. A multinomial model (MODEL-2) developed using the stated preference(SP) data was tested for the use of future transportation mode. It is four that the those models provides reasonable results in terms of behavioral and statistical consideration.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.27-35
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• 2018

This study analyzed the factors influencing the choice of new farming type in order to prepare the countermeasures against structural changes of farm labor force. The analytical model was the multinomial logit model(MNL). The test for Independence and Irrelevance Alternatives(IIA) assumption in MNL shows that the IIA assumption in our data is rejected. Alternatively, we chose the multinomial probit model(MNP) that does not assume IIA. Data were obtained from 2010 census of Agriculture, Forestry and Fisheries of Statistics Korea. New farming types are succession(13.9%), return-to-farming(45.0%), part-time-farming(32.5%) and etc(8.6%). Analysis results showed that the characteristics of farms, commodity, management, and region influenced the choice of new farming type. This study is expected to help policy makers to produce support policies by new farming types in order to increase the number of new farmers and to make them easier to settle down in agriculture.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.557-573
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• 2019

Mortality study is an essential component of actuarial risk management for life insurance policies, annuities, and pension plans. Life expectancy has drastically increased over the last several decades; consequently, longevity risk associated with annuity products and pension systems has emerged as a crucial issue. Among the various aspects of mortality study, a consideration of the cause-of-death mortality can provide a more comprehensive understanding of the nature of mortality/longevity risk. In this case study, the cause-of-mortality data in Korea and the US were analyzed along with a multinomial logistic regression model that was constructed to quantify the impact of mortality reduction in a specific cause on actuarial values. The results of analyses imply that mortality improvement due to a specific cause should be carefully monitored and reflected in mortality/longevity risk management. It was also confirmed that multinomial logistic regression model is a useful tool for analyzing cause-of-death mortality for actuarial applications.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.435-451
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• 2001

We describe a hierarchical bayesian model to analyze multinomial nonignorable nonresponse data. Using a Dirichlet and beta prior to model the cell probabilities, We develop a complete hierarchical bayesian analysis for multinomial proportions without making any algebraic approximation. Inference is sampling based and Markove chain Monte Carlo methods are used to perform the computations. We apply our method to the dta on body mass index(BMI) and show the model works reasonably well.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.217-223
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• 2016

Outcome misclassification is widespread in classification problems, but methods to account for it are rarely used. In this paper, the problem of inference with misclassified multinomial logit data with a large number of multinomial parameters is addressed. We have had a significant swell of interest in the development of novel methods to infer misclassified data. One simulation study is shown regarding how seriously misclassification issue occurs if the number of categories increase. Then, using the group lasso regression, we will show how the best model should be fitted for that kind of multinomial regression problems comprehensively.

• Knowledge Management Research
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• v.14 no.1
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• pp.73-94
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• 2013

Customer's purchase state consists of purchase inertia and variety-seeking. As the growing brand familiarity triggers the increase of brand attractiveness, customers purchase state will be of inertia. However the excessively growing brand familiarity ignites the decrease of brand attractiveness. Followingly the purchase state will be tend to plunge into the variety-seeking state. The main topic of this study is to validate the asymmetric formation of customer's purchase states between inertia and variety-seeking. In order to follow up the main topic, this article introduces a model to freely describe the velocity of value changes depending upon the purchase states. This model will help overcome the limitation of the past studies having been based on the symmetric value changes. Based on this approach marketer will be able to decide the timing of sales promotions. This research utilized local telecommunication carrier's database of smartphone application purchase/download records. This database was collected from two years (2009 and 2010) span, the time when the smartphones started commodifying in Korea whilst most of the past studies had used purchase data of maturity stage products. From this approach utilizing the introduction stage data in the product life cycle, the probability of brand choice depending upon the purchase state on the early-stage can be probed. Moreover, this study tries to expand the research methodology from the other areas of research by knowledge sharing. Here this study introduces the methodology of section-aggregated multinomial logit to simultaneously estimate the parameters that were included in the plural multinomial logit functions while the plural functions were inter-connected. This adoption of section-aggregated multinomial logit model procedures from the computerized statistics areas is expected to nourish the marketing research for more precise analysis and estimation of effects of marketing activities.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.507-517
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• 2019

Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.125-137
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• 2005

Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.