• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.731-743
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• 2009

The interval estimation for binomial proportion has been treated practically as well as theoretically for a long time. In this paper we compared the properties of major confidence intervals and summarized current issues for coverage probability and interval length which are the criteria of evaluation for confidence interval. Additionally, we examined the three topics which were considered in using the binomial confidence interval in the field. And finally we discussed the future studies for a low binomial proportion.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.615-623
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• 2011

Several methods are used in interval estimation for binomial proportion; however the coverage probabilities of most confidence intervals depart from the confidence level when the binomial population proportion closes to 0 or 1 due to the extreme value. Vollset (1993), Agresti and Coull (1998), Newcombe (1998), and Brown et al. (2001) suggested methods to adjust the extreme value. This paper discusses the influence of extreme value in a binomial confidence interval through the numerical comparison of 6 confidence intervals.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.607-615
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• 2005

Recently the interval estimation of a binomial proportion is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the will-known Wald confidence interval. Various alternatives have been proposed. Among them, Agresti-Coull confidence interval has been recommended by Brown et al. (2001) with other confidence intervals for large sample, say n $\ge$ 40. On the other hand, a noninformative Bayesian approach called Polya posterior often produces statistics with good frequentist's properties. In this note, an interval estimator is developed using weighted Polya posterior. The resulting interval estimator is essentially the Agresti-Coull confidence interval with some improved features. It is shown that the weighted Polys posterior produce an effective interval estimator for small sample size and a severely skewed binomial distribution.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.217-230
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• 2006

e discuss proper confidence intervals for interval estimation of a low binomial proportion. A large sample surveys are practically executed to find rates of rare diseases, specified industrial disaster, and parasitic infection. Under the conditions of 0 < p ${\leq}$ 0.1 and large n, we compared 6 confidence intervals with mean coverage probability, root mean square error and mean expected widths to search a good one for interval estimation of population proportion p. As a result of comparisons, Mid-p confidence interval is best and AC, score and Jeffreys confidence intervals are next.

• The Journal of Bigdata
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• v.3 no.2
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• pp.101-112
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• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.171-181
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• 2006

The Wald confidence interval has been considered as a standard method for the difference of proportions. However, the erratic behavior of the coverage probability of the Wald confidence interval is recognized in various literatures. Various alternatives have been proposed. Among them, Agresti-Caffo confidence interval has gained the reputation because of its simplicity and fairly good performance in terms of coverage probability. It is known however, that the Agresti-Caffo confidence interval is conservative. In this note, a confidence interval is developed using the weighted Polya posterior which was employed to obtain a confidence interval for the binomial proportion in Lee(2005). The resulting confidence interval is simple and effective in various respects such as the closeness of the average coverage probability to the nominal confidence level, the average expected length and the mean absolute error of the coverage probability. Practically it can be used for the interval estimation of the difference of proportions for any sample sizes and parameter values.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.606-609
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• 2008

Traditionally, the feasibility of the private investment is determined by NPV(Net Presented Value) based on DCF(Discounted Cash Flow) and the volume of government's subsidiary without quantifying the effect of overturn share ratio and MRG(Minimum Revenue Guarantee), these variables which can seriously effect on the economic feasibility. One of the most important reasons why these variables are not underestimated is that the quantifying methods are insufficiently or so complicatedly studied to apply practically the real project. Therefore, this study suggests the modified binominal option model to estimate the overturn share ratio and MRG and estimates how much these variables impact the private investment. Also, these results are helpful to estimate how much the government's subsidiary can be reduced.

• Korean journal of applied entomology
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• v.40 no.3
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• pp.197-202
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• 2001

The density of citrus red mite(CRM), Panonychus citri(McGregor), on the commercial satsuma mandarin Citrus unshiu L. groves were determined by counts of the number of CRM per leaf using by leaf sample in Jeju for 2 years. Binomial sampling plans were developed based on the relationship between the mean density per leaf(m) and the proportion of leaf infested with less than T mites per leaf($P_{T}$), according to the empirical model $ln(m)={\alpha}+{\beta}ln(-ln(1-P_{T}))$. T was defined as tally threshold, and set to 1, 3, 5 and 7 mites per leaf in this study. Increasing sample size, regardless of tally threshold, had little effects on the precision of the binomial sampling plan. Increasing sampling size had little effect on the precision of the estimated mean regardless of tally thresholds. T=1 was chosen as the best tally threshold for estimating densities of CRM based on the precision of the model. The binomial model with T=1 provided reliable predictions of mean densities of CRM observed on the commercial satsuma mandarin groves. Binomial sequential sampling procedure were developed for classifying the density of CRM. A binomial sampling program for decision-making CRM population level based on action threshold of 2 mites per leaf was obtained.

• Korean journal of applied entomology
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• v.37 no.2
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• pp.151-157
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• 1998

Infestations of two spotted spider mite (TSSM), Tetranychus urticae Koch, on glasshouse rose (Rosa sp.) grown by an arching method, were determined by counts of the number of TSSM per leaflet in Buyeo, Chungnam Province, for a 2-yr period. Binomial sampling plans were developed based on the relationship between mean density per leaflet (m), and proportion of leaflets infested with ( T mites (PT), according to the empirical model In (m) = a+p In (-ln (1 -PT)). T was defined as tally threshold, and set to 1, 3, 5, 7, and 9 mites per leaflet. Increasing sample size had little effects on the precision of the binomial sampling plan, regardless of tally threshold. However, the precision increased with higher tally thresholds. There was a negligible improvement in precision with T ) 7 mites per leaflet. T= 7 was chosen as the best tally threshold for estimating densities of TSSM based on the precision of the model. Independent data set was used to evaluate the model. The binomial model with T= 7 provided reliable predictions of mean densities of TSSM observed on the commercial glasshouse roses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.2
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• pp.333-340
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• 2016

The aim of this study is to specifically understand the characteristics of the crime by empirical analysis for the determining factors that affect determining the crime through the space syntax in Busan. In this study, poisson regression and negative binomial regression were used for accurate analysis. 8 variables that were significant of the total 13 variables. The summary if this study based on the results is as follow. Statistically significant variables are female ratio, over 65 population ratio, administration are and commercial area ratio in characteristics. And the more CCTVs a region has, the lower crime rate it shows. As a results of examing whether space syntax variables can predict crime occurrence places. Space with low connectivity come to be a crime causal factor because they have few other related spaces and thereby have low possibility of sudden appearance of interrupters, which results in low surveillance levels of foot passengers. It will provide the basic data that can contribute to urban planning and implementation of crime prevention aspects.