• Journal of the Korean Statistical Society
• /
• v.25 no.1
• /
• pp.111-122
• /
• 1996

A model for a system whose state changes continuously with time is introduced. It is assumed that the system is modeled by a Brownian motion with negative drift and an absorbing barrier at the origin. A repairman arrives according to a Poisson process and repairs the system according to an (s,S) policy, i.e., he increases the state of the system up to S if and only if the state is below s. A partial differential equation is derived for the distribution function of X(t), the state of the system at time t, and the Laplace-Stieltjes transform of the distribution function is obtained by solving the partial differential equation. For the stationary case the explicit expression is deduced for the distribution function of the stationary state of the system.

• Asian-Australasian Journal of Animal Sciences
• /
• v.14 no.7
• /
• pp.910-914
• /
• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1221-1229
• /
• 2014

In this paper we choose the best model among several bivariate Poisson models on Korean soccer data. The models considered allow for correlation between the number of goals of two competing teams. We use an R package called bivpois for bivariate Poisson regression models and the data of K-league for season 1983-2012. Finally we conclude that the best fitted model supported by the AIC and BIC is the bivariate Poisson model with constant covariance. The zero and diagonal inflated models did not improve the model fit. The model can be used to examine home-away effect, goodness of fit, attack and defense parameters.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.6
• /
• pp.1338-1342
• /
• 2011

In this paper, threshold voltage characteristics have been analyzed as one of short channel effects occurred in double gate(DG)MOSFET to be next-generation devices. The Gaussian function to be nearly experimental distribution has been used as carrier distribution to solve Poisson's equation, and threshold voltage has been investigated according to projected range and standard projected deviation, variables of Gaussian function. The analytical potential distribution model has been derived from Poisson's equation, and threshold voltage has been obtained from this model. Since threshold voltage has been defined as gate voltage when surface potential is twice of Fermi potential, threshold voltage has been derived from analytical model of surface potential. Those results of this potential model are compared with those of numerical simulation to verify this model. As a result, since potential model presented in this paper is good agreement with numerical model, the threshold voltage characteristics have been considered according to the doping profile of DGMOSFET.

• The Korean Journal of Applied Statistics
• /
• v.23 no.5
• /
• pp.835-844
• /
• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.105-107
• /
• 2015

In this paper, we proposed LTD(Load Tolerance Density-distribution) algorithm using dynamic density for analyzing distribute routing path. MANET(Mobile Ad-hoc Networks) consists of the node that has a mobility. By the Mobility, the topology is exchanged frequently. To reduce the exchange of topology, the hierarchy network is studied. However, if the load is concentrated at the cluster head node, the communication is disconnected. the proposed algorithm measure the dynamic density of the node using poisson distribution. And this algorithm provides distribute routing path using dynamic density. The simulation results, the proposed algorithm shows improved packet delivery ratio than the compared algorithm.

• Structural Engineering and Mechanics
• /
• v.90 no.6
• /
• pp.543-549
• /
• 2024

The utilization of the fabric materials for lightweight building structures has attracted considerable attention due to the multiple functions and high strength-to-weight ratio. The mechanical properties of the fabric materials evolve with the loading cycle, especially for the Poisson's ratio that requires the full cyclic strain to determine the accurate values. The digital image correlation method has been justified but needs to meet the flexibility and complexity requirements of the fabric materials. This paper thus proposes a modified digital image correlation method to quantify the Poisson's ratio of fabric materials. To obtain the accurate Poisson's ratio of fabric materials in the cyclic experiments using non-contact measuring method, a speckle generation of the digital image correlation method is implemented to obtain the strain distribution and strain characteristics. The uniaxial cyclic experiments for the fabric materials are carried out in the warp, weft and 45° directions. The digital image correlation photos are taken when the material properties become stable in the cyclic loading. The results show that the strain distributions are non-uniform and dependent on the specimen directions. The reliable Poisson's ratios of the fabric materials in the warp, weft and 45° directions are 0.016, 1.2 and 2.6. The strain asymmetry at the maximum strain position is related with the weaving architecture. These observations and results are indispensable to understand the Poisson's ratios of fabric materials and to guide the proper analysis of the large-span membrane structures.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.1
• /
• pp.29-35
• /
• 2011

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting scheme in practice. And CUSUM-chart is used when it is desirable to detect out of control situations very quickly because of sensitive to a small or gradual drift in the process. In this paper, I compare CUSUM-chart to X-chart for the Katz family covering equi-, under-, and over-dispersed distributions relative to the Poisson distribution.

• Communications for Statistical Applications and Methods
• /
• v.25 no.2
• /
• pp.173-184
• /
• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.291-309
• /
• 2023

Longitudinal count data has been widely collected in biomedical research, public health, and clinical trials. These repeated measurements over time on the same subjects need to account for an appropriate dependency. The Poisson regression model is the first choice to model the expected count of interest, however, this may not be an appropriate when data exhibit over-dispersion or under-dispersion. Recently, Conway-Maxwell-Poisson (CMP) distribution is popularly used as the distribution offers a flexibility to capture a wide range of dispersion in the data. In this article, we propose a Bayesian CMP regression model to accommodate over and under-dispersion in modeling longitudinal count data. Specifically, we develop a regression model with random intercept and slope to capture subject heterogeneity and estimate covariate effects to be different across subjects. We implement a Bayesian computation via Hamiltonian MCMC (HMCMC) algorithm for posterior sampling. We then compute Bayesian model assessment measures for model comparison. Simulation studies are conducted to assess the accuracy and effectiveness of our methodology. The usefulness of the proposed methodology is demonstrated by a well-known example of epilepsy data.