• Communications for Statistical Applications and Methods
• /
• v.16 no.3
• /
• pp.501-509
• /
• 2009

Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.

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

• Communications for Statistical Applications and Methods
• /
• v.11 no.3
• /
• pp.619-629
• /
• 2004

Binomial group testing or composite sampling is often used to estimate the proportion, p, of positive(infects, defectives) in a population when that proportion is known to be small; the potential benefits of group testing over one-at-a-time testing are well documented. The literature has focused on maximum likelihood estimation. We provide two Bayes estimators and compare them with the MLE. The first of our Bayes estimators uses an uninformative Uniform (0, 1) prior on p; the properties of this estimator are poor. Our second Bayes estimator uses a much more informative prior that recognizes and takes into account key aspects of the group testing context. This estimator compares very favorably with the MSE, having substantially lower mean squared errors in all of the wide range of cases we considered. The priors uses a Beta distribution, Beta ($\alpha$, $\beta$), and some advice is provided for choosing the parameter a and $\beta$ for that distribution.

• Analyses & Alternatives
• /
• v.4 no.1
• /
• pp.105-123
• /
• 2020

This study analyzes the risk selection behavior of Japanese households. The study approaches the view of 'the hyperbolic discount' which is used in behavioral economics based on the rise in mortgage lending by low-income households in the late 2000s. The study focuses on how households risk preferences vary by income levels. The study analyzes the relationship of attitude of household interest rate risk using Binomial Logistic and Heckman two-step estimation method assuming that there are only two types of Adjustable-Rate Mortgage and Fixed-Rate Mortgage. As a result of the empirical analysis, low-income households annual income tend to have a higher proportion of housing debt as same as higher interest rate risk preferences households in proportion to income growth and interest rate risk preferences. Those results indicate that there is possibility of a hyperbolic discount on low-income households in Japan, and support the hypothesis that low-income households are relatively higher household debt ratio because of high utility due to home purchase in the near future (short-term).

• Ocean and Polar Research
• /
• v.39 no.2
• /
• pp.73-84
• /
• 2017

The probability distribution of ichthyoplankton is important for enhancing the precision of sampling while reducing unnecessary surveys. To estimate the ichthyoplankton detection proportion (IDP) and its margin of error (ME), the monitoring information of the chub mackerel's (Scomber japonicus) ichthyoplankton presence-absence sampling data has been were collected over approximately 30 years (from 1982 to 2011) in the Korean coastal seas. Based on the computed spatial distributions of the mackerel's IDP and ME, the confidence interval (CI) range, defined as 2 ME, decreases from approximately 80% to 40% as the sample size n increases from 4 to 24 and the ME is approximately 40% in the typical (seasonal survey) case n = 4 per year. The IDP and ME off Jeju Island are relatively high at the 0.5-degree smoothing level. After increasing the spatial smoothing level to 1.0-degree, the ME decreased, and the spatial distribution pattern also changed due to the over-smoothing effects. In this study, the 0.5-degree smoothing is more suitable for the distribution pattern than the 1.0-degree smoothing level. The area of the high IDP and the low ME on the mackerel's ichthyoplankton was similar to the estimated spawning ground in the Korean peninsula. This information could contribute to enhancing for the spawning ecology surveys.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.943-952
• /
• 2013

Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.579-588
• /
• 2021

Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.

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

• Korean journal of applied entomology
• /
• v.55 no.4
• /
• pp.405-412
• /
• 2016

Infestation of adults and pupae of sweetpotato whitefly, Bemisia tabaci, on paprika (Capsicum annuum var. angulosum) grown in greenhouses in Jinju, Gyeongnam province during 2014was determined by counts of the number of target stage of B. tabaci per leaflet. Binomial sampling plans were developed based on the relationship between the mean density per leaflet (m) and the proportion of leaflets infested with less than T whiteflies ($P_T$), according to the empirical model $(({\ln}(m)={\alpha}+{\beta}({\ln}(-{\ln}(1-P_T))))$. T was defined as the tally threshold, and set to 1, 2, 3, 4, 5 (adults) and 1, 3, 5, 7 (pupae) per leaflet in this study. Increasing the sample size, regardless of tally threshold, had little effect on the precision of the binomial sampling plan. Based on the precision of the model, T = 1 was chosen as the best tally threshold for estimating densities of B. tabaci adults and T = 3 was best tally threshold in B. tabaci pupae. Using the results obtained in the greenhouse, a simulated validation of the developed sampling plan by RVSP (Resampling Validation for Sampling Plan) demonstrated the plan's validity. Above all, the binomial model with T = 1 and T = 3 provided reliable predictions of the mean densities of B. tabaci adults and pupae on greenhouse paprika.

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