• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.8
• /
• pp.1-6
• /
• 2022

This paper uses the gravity model estimated by the random effect method to analyze the factors affecting Vietnam's coffee export turnover for the period 2007-2020 major markets according to statistics from the General Statistics Office and the General Department of Customs. Coffee export turnover was collected from the General Statistics Office, General Department of Customs, and Vietnam Cacao Coffee Association. The authors calculated the price of coffee based on output and export value from data on coffee export turnover; the authors calculated the economic gap based on population and Gross Domestic Product data (reference: geographic distance metrics on the website: http://www.distancefromto.net/countries.php) and other data was collected based on the databases of the Food and Agriculture Organization of the United Nations, the International Monetary Fund, and World Bank organizations. The results of the study show that from 2007 to 2020, the factors of Vietnam's export price of coffee, geographical distance, Gross Domestic Product of the importing country and Gross Domestic Product of Vietnam, the population of Vietnam, the economic gap between Vietnam and the importing country, the openness of the economy, all have an impact on Vietnam's coffee export turnover. Finally, some conclusions about the policy's impact are made based on the empirical results of the paper.

• Interdisciplinary Bio Central
• /
• v.2 no.2
• /
• pp.4.1-4.6
• /
• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

• Atmosphere
• /
• v.28 no.3
• /
• pp.247-261
• /
• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.

• The Korean Journal of Applied Statistics
• /
• v.36 no.5
• /
• pp.399-413
• /
• 2023

Recently, monitoring and detecting anomalies in social networks have become an interesting research topic. In this study, we investigate the detection of abnormal changes in a network modeled by the DCSBM (degree corrected stochastic block model), which reflects the propensity of both individuals and communities. To this end, we propose three methods for anomaly detection in the DCSBM networks: One method for monitoring the entire network, and two methods for dividing and monitoring the network in consideration of communities. To compare these anomaly detection methods, we design and perform simulations. The simulation results show that the method for monitoring networks divided by communities has good performance.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.4
• /
• pp.153-158
• /
• 2007

This paper presents a new approach to noise estimation to improve speech enhancement in non-stationary noisy environments. The proposed method combines the two separate noise power estimates provided by the minimum statistics (MS) for speech presence and soft decision (SD) for speech absence in accordance with SAP (Speech Absence Probability) on a separate frequency bin. The performance of the proposed algorithm is evaluated by the subjective test under various noise environments and yields better results compared with the conventional MS or SD-based schemes.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.148-155
• /
• 2008

In this paper, we constructed the noise robust content-based music retrieval system in mobile environment. The performance of the proposed system was verified with ZCPA feature which is blown to have noise robust characteristic in speech recognition application. In addition, new indexing and fast retrieval method are proposed to improve retrieval speed about 99% compare to exhaustive retrieval for large music DB. From the computer simulation results in noise environment of 15dB - 0dB SNR, we confirm the superior performance of the proposed system about 5% - 30% compared to MFCC and FBE(filter bank energy) feature.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.479-496
• /
• 2003

We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

• Journal of the Korean Statistical Society
• /
• v.28 no.3
• /
• pp.311-324
• /
• 1999

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1139-1148
• /
• 2011

Team standings in the regular professional baseball league should be determined based on a reasonable criterion; however, an unreasonable Japanese method is being used in Korea as of 2011. In this paper, we suggest a new method of computing the percentage of victories constructed by combining the advantages of the methods to determine team standings used in Korean professional baseball. We also have applied preexistent methods and suggested method to past and present Korean professional baseball data.

• Communications for Statistical Applications and Methods
• /
• v.27 no.1
• /
• pp.97-108
• /
• 2020

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.