• Asian-Australasian Journal of Animal Sciences
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• v.11 no.6
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• pp.642-647
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• 1998

A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• Ocean and Polar Research
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• v.36 no.3
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• pp.289-302
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• 2014

This paper aims to evaluate the indirect economic damages to anglers of the marine recreational charter caused by marine pollution associated with the Herbei Spirit vessel, which spilled 12,547 kl of crude oil in Taean coastal areas in December 2007. In order to evaluate the indirect cost to anglers of the charter fishing, consumer surplus for charter fishing is estimated using a Poisson model (PM), a negative binomial model (NBM), a truncated Poisson model (TPM), and a truncated negative binomial model (TNBM), which account for the characteristics of count data (non-negative discrete data), for individual travel cost method (ITCM). Because of over-dispersion problem in PM and TPM, NBM and TNBM are considered to be more appropriate statistically. All parameters such as income, fishing careers, travel cost and catch that are estimated are statistically significant and theoretically valid. Based on TNBM results, consumer surplus per trip and per person was estimated to be 277 thousand won, total consumer surplus per person and per year about 2.3 million won, and the marginal effect of consumer surplus on % changes in catch rate is about 33 thousand won. The consumer surplus was converted into total indirect economic damages for aggregation which are evaluated to be 125 billion won, reflecting the number of anglers and damage rate.

• Ocean and Polar Research
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• v.30 no.2
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• pp.141-148
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• 2008

This paper aims at estimating consumer surplus for recreational sea fishing in Tongyeong coastal area using individual travel cost method. A Poisson model (PM), a negative binomial model (NBM), a truncated Poisson model (TPM), and a truncated negative binomial model (TNBM) are applied for individual travel cost method in order to account characteristics of count data (non-negative discrete data.) The survey was conducted for 462 inshore anglers using personal interview method in Tongyeong during July and October 2007. Respondents were asked about how often they do fishing, travel costs, catch, income, and so on. Because of over-dispersion problem in PM and TPM, NBM and TNBM were considered to be more appropriate statistically. All parameters estimated are statistically significant and theoretically valid. As the results based on TNBM, consumer surplus per trip was estimated to be 183,486 won, total consumer surplus per person and per year 3,399,658 won, and the marginal effect of consumer surplus on % changes in catch rate is 185,372 won.

• Asian Journal of Innovation and Policy
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• v.3 no.1
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• pp.94-116
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• 2014

The technological innovation is considered as an important factor and there is a positive externality in developing technology in the form of technology spillover. In this context, it is argued that government should play an active role in advancing technology development and several means have been introduced. This study attempts to analyze manufacturing firms' evaluation for the performance of government assistance programs to their R&D activities. Considering that the performance evaluation takes the form of a count outcome, we apply several kinds of count data models. Some interesting findings emerge from the analysis. For example, we found that a firm's sales amount, dummy for the firm's having an R&D department, dummy for the firm's being a venture one, and the number of the firm's innovative activities have positive relationships with the degree that the firm evaluates government assistance as being useful.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.895-900
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• 2013

In this research, we propose a simple bivariate zero inflated negative binomial regression model with different dispersion for bivariate count data with excess zeros. An application to the demand for health services shows that the proposed model is better than existing models in terms of log-likelihood and AIC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.357-366
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• 2007

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.111-118
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• 2014

PURPOSES: The purposes are to analyze the pedestrian accident severity and to develop the accident models by arterial road function. METHODS: To analyze the accident, count data and ordered logit models are utilized in this study. In pursuing the above, this study uses pedestrian accident data from 2007 to 2011 in Cheongju. RESULTS : The main results are as follows. First, daytime, Tue.Wed.Thu., over-speeding, male pedestrian over 65 old are selected as the independent variables to increase pedestrian accident severity. Second, as the accident models of main and minor arterial roads, the negative binomial models are developed, which are analyzed to be statistically significant. Third, such the main variables related to pedestrian accidents as traffic and pedestrian volume, road width, number of exit/entry are adopted in the models. Finally, Such the policy guidelines as the installation of pedestrian fence, speed hump and crosswalks with pedestrian refuge area, designated pedestrian zone, and others are suggested for accident reduction. CONCLUSIONS: This study analyzed the pedestrian accident severity, and developed the negative binomial accident models. The results of this study expected to give some implications to the pedestrian safety improvement in Cheongju.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.603-613
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• 2008

We often encounter the situation that discrete count data have a large portion of zeros. In this case, it is not appropriate to analyze the data based on standard regression models such as the poisson or negative binomial regression models. In this article, we consider Bayesian analysis for two commonly used models. They are zero-inflated poisson and negative binomial regression models. We use the Bayes factor as a model selection tool and computation is proceeded via Markov chain Monte Carlo methods. Crash count data are analyzed to support theoretical results.