• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.303-315
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• 2017

Yoo (2015, Statistics and Probability Letters, 99, 109-113) derives theoretical results in an optimal sufficient dimension reduction with singular inner-product matrix. The results are promising, but Yoo (2015) only presents one simulation study. So, an evaluation of its practical usefulness is necessary based on numerical studies. This paper studies the asymptotic behaviors of Yoo (2015) through various simulation models and presents a real data example that focuses on ordinary least squares. Intensive numerical studies show that the $x^2$ test by Yoo (2015) outperforms the existing optimal sufficient dimension reduction method. The basis estimation by the former can be theoretically sub-optimal; however, there are no notable differences from that by the latter. This investigation confirms the practical usefulness of Yoo (2015).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1479-1481
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• 2003

The aim of this paper was mainly to interpret the real information-missing patch of image by using the kriging interpolation technology of spatial statistics. The TM Image of the Jingouling Forest Farm of Wangqing Forestry Bureau of Northeast China on 1 July 1997 was used as the tested material in this paper. Based on the classification for the TM image, the information pixel-missing patch of image was interpolated by the kriging interpolation technology of spatial statistics theory under the image treatment software-ERDAS and the geographic information system software-Arc/Info. The interpolation results were already passed precise examination. This paper would provide a method and means for interpreting the information-missing patch of image.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.333-348
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• 2015

In this paper, we study a generalization of the modified Weibull distribution. The generalization follows the recent work of Cordeiro et al. (2013) and is based on a class of exponentiated generalized distributions that can be interpreted as a double construction of Lehmann. We introduce a class of exponentiated generalized modified Weibull (EGMW) distribution and provide a list of some well-known distributions embedded within the proposed distribution. We derive some mathematical properties of this class that include ordinary moments, generating function and order statistics. We propose a maximum likelihood method to estimate model parameters and provide simulation results to assess the model performance. Real data is used to illustrate the usefulness of the proposed distribution for modeling reliability data.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.183-195
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• 1995

For a distribution on the unit sphere, the set of eigenvectors of the second moment matrix is a conventional measure of orientation. Asymptotic confidence cones for eigenvector under the parametric assumptions for the underlying distributions and nonparametric confidence cones for eigenvector based on bootstrapping were proposed. In this paper, to reduce the level error of confidence cones for eigenvector, double bootstrap confidence cones based on prepivoting are considered, and the consistency of this method is discussed. We compare the perfomances of double bootstrap method with the others by Monte Carlo simulations.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.2
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• pp.261-272
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• 2006

This work examines the delimitation of metropolitan areas in Canada, focusing on the Census Metropolitan Areas/Census Agglomerations(CMAs/CAs) that the Statistics Canada defines every Census year. The CMA/CA is built upon the functional-area method which is among the three approaches (i.e., density-based, land use-based, functional-area approaches) to the definition of an urban area. Importantly, the delimitation of a CMA/CA employs the Urban Area(UA) which the Statistics Canada defines via density-based and land use-based methods. In particular, the UA which has 10,000 or more residents is the urban core of a CMA/CA. Our examination of the CMA/CA in Canada also presents some points to be considered with regard to the delimitation of metropolitan areas in Korea which has yet to be implemented.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.3
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• pp.300-309
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• 2011

Purpose: The purpose of this study was to investigate facilitators of evidence-based practice (EBP) in Clinical Nurses. Method: The instruments used in study were the EBP facilitator scale developed by Nagy et al. and a questionnaire on EBP-related characteristics. Data were collected from 230 nurses at a tertiary hospital and analyzed by descriptive statistics. Results: Compared to previous studies, this study showed that nurses had more experience related to research courses and clinical research. However, the proportion of nurses who reviewed relevant articles still remained low. The respondents had positive perceptions of organizational supports for EBP and belief in the value of EBP, whereas they had negative perceptions of skills in locating and evaluating research reports, knowledge of research terms and statistics, and time to devote to EBP. Conclusion: The findings of the study provide important basic data to develop and implement an EBP programs. In future, EBP programs should cover the nurses' skills to search and review research literature as well as their knowledge of research terms and statistics. Furthermore, nurses will require help to ensure that there is adequate time to devote to EBP.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.1-6
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• 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
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• v.2 no.2
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• pp.4.1-4.6
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• 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
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• v.28 no.3
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• pp.247-261
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• 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.