• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.9-17
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• 2010

Decision problem called an optimal release policies, after testing a software system in development phase and transfer it to the user, is studied. The applied model of release time exploited infinite non-homogeneous Poisson process. This infinite non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The failure life-cycle distribution used superposition which has various intensity, if the system is complicated. Thus, software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time. Through this study, in terms of superposition model and simply model, the optimal time to using superposition model release the software developer to determine how much could count will help.

• The Journal of Fisheries Business Administration
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• v.36 no.2 s.68
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• pp.121-134
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• 2005

In order to evaluate the effectiveness of management measures and to provide policy suggestions for the allocation of total allowable catch between recreational and commercial sectors, the economic value of red grouper recreational fishery in the United States Gulf of Mexico was estimated using a Travel Cost Method(TCM), Due to the characteristic of count data, a Poisson model(PM) and a Negative binomial model(NBM) were used in the TCM. Results of models showed that the NBM was statistically more suitable than the PM since the overdispersion problem occurred in the PM. Results also indicated all signs of the estimated parameters were as expected and were significant, except for a Boat parameter in both models. Based on the results of NBM, the total economic value of the recreational red grouper fishery was estimated to be $\$698.6$ and the value per trip was $\$179.5$. In addition, the total changes in expected consumer surplus due to changes in catch rates was $ \$42.3$.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1087-1097
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• 2017

This study aims to find the best model to fit the number of insurance solicitor's turnovers of life insurance companies using count data regression models such as poisson regression, negative binomial regression, zero-inflated poisson regression, or zero-inflated negative binomial regression. Out of the four models, zero-inflated negative binomial model has been selected based on AIC and SBC criteria, which is due to over-dispersion and high proportion of zero-counts. The significant factors to affect insurance solicitor's turnover found to be a work period in current company, a total work period as financial planner, an affiliated corporation, and channel management satisfaction. We also have found that as the job satisfaction or the channel management satisfaction gets lower as channel management satisfaction, the number of insurance solicitor's turnovers increases. In addition, the total work period as financial planner has positive relationship with the number of insurance solicitor's turnovers, but the work period in current company has negative relationship with it.

• Journal of East-West Nursing Research
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• v.28 no.2
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• pp.160-169
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• 2022

Purpose: This study was conducted to identify predictors related to aggressive behavior of patients with mental illness admitted to a closed psychiatric ward. Methods: This study adopted a retrospective design which analyzed the hospital medical records of 363 patients with mental illness admitted to the psychiatric closed ward of a university hospital in Seoul, Korea. The collected data were analyzed using SPSS IBM 20.0 and STATA 12.0 SE. ZIP (Zero-Inflated Poisson) and count data analysis were used for the factor influencing the occurrence and frequency of aggressive behavior. Results: The results of ZIP model showed that the factors influencing non-probability of aggressive behavior were anxiety, non-adherence, and frustration. In addition, the factors influencing frequency of aggressive behavior were bipolar disorder and personality disorder trait. Conclusion: We found that bipolar disorder, frustration, and non-adherence are more likely to increase the likelihood of aggressive behavior in patients with mental illness. In particular, patients diagnosed with bipolar disorder were 1.95 times more likely to engage in repetitive aggressive behavior compared to those without a diagnose. However, since the results were different form previous studies, further studies on the traits of anxiety and personality disorders are needed.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.15-22
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• 2003

In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.177-187
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• 2020

This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

• The Journal of Fisheries Business Administration
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• v.36 no.1 s.67
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• pp.81-101
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• 2005

This article is to estimate the fishing frequency function in Korean recreational fishery with respect to socio-economic characteristics of anglers. First, the study described the characteristics of the entire angler population on the view points of 9 socio-economic variables. And then, the study divided the total angler population into three groups of in-land, sea, and mixed angler populations in order to investigate the differences in their characteristics. The study could confirm the existence of differences in regions, size of regions, and educational levels between the in - land and the sea angler populations by testing heterogeneity in the frequency table. The fishing frequency function is estimated using Poisson regression model in order to accomodate the count data(non-negative discrete random variable) aspects of the fishing frequency. However, the model specification error is found due to overdispersion of data. The model exhibits the lack of goodness of fit. The negative binomial regression model is adopted to cure the overdispersion of the data as an alternative estimation methodology. Finally, the study can confirm overdispersion does not exist in the model any more and the goodness of fit improved significantly to the reasonable level. The results of estimation of fishing frequency population modeled by the negative binomial regression models are following. The three variables of region, sex, and education have effects on the decision making process of fishing frequency in the case of in-land recreation fishery. On the other hand, the three variables of sex, age, and marriage status do the same job in the case of sea angler population. Among the left-over variables, both income and use of Internet variables now affect on the process in mixed angler population. Finally, the results of whole angler population show that all of the previous variables are proven to be statistically significant due to the summation of data with all three sub-groups of angler population.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.93-103
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• 2024

Mongolia's diverse geographical landscape and harsh climate make it particularly susceptible to various natural disasters, including forest fires, heavy rains, dust storms, and heavy snow. This study aims to explore the relationships between key climatic variables and the frequency of these disasters. We collected monthly data from January 2022 to April 2024, encompassing average temperature, temperature variability (absolute temperature difference), average humidity, and precipitation across the capitals of Mongolia's 21 provinces and the capital city Ulaanbaatar. The data were analyzed using multiple statistical models: Linear Regression, Poisson Regression, and Negative Binomial Regression. Descriptive statistics provided initial insights into the variability and distribution of the climatic variables and disaster occurrences. The models aimed to identify significant predictors and quantify their impact on disaster frequencies. Our approach involved standardizing the predictor variables to ensure comparability and interpretability of the regression coefficients. Our findings indicate that climatic variables significantly affect the frequency of natural disasters. The Negative Binomial Regression model was particularly suitable for our data, which exhibited overdispersion common characteristic in count data such as disaster occurrences. Understanding these relationships is crucial for developing targeted disaster management strategies and policies to mitigate the adverse effects of climate change on Mongolian communities. This research provides valuable insights into how climatic changes impact disaster occurrences, offering a foundation for informed decision-making and policy development to enhance community resilience.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.837-849
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• 2019

Latent class models (LCM) are useful tools to draw hidden information from categorical data. This model can also be interpreted as a mixture model with multinomial component distributions. In some cases, however, an available dataset may contain both categorical and count or continuous data. For such cases, we can extend the LCM to a mixture model with both multinomial and other component distributions such as normal and Poisson distributions. In this paper, we consider a LCM for the data containing categorical and count data to analyze the Drug Review dataset which contains categorical responses and text review. From this data analysis, we show that we can obtain more specific hidden inforamtion than those from the LCM only with categorical responses.