• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.237-248
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• 2009

The probability of default on the credit evaluation study is represented as a linear combination of two distributions of default and non-default, and the distribution of the probability of default are generally known in most cases. Except the well-known Kolmogorov-Smirnov statistic for testing the identity of two distribution, Kuiper, Cramer-Von Mises, Anderson-Darling, and Watson test statistics are introduced in this work. Under the assumption that the population distribution is known, modified Cramer-Von Mises, Anderson-Darling, and Watson statistics are proposed. Based on score data generated from various probability density functions of the probability of default, the modified test statistics are discussed and compared.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.517-541
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• 2019

According to the OECD (2015) and UN (2017), Korea was classified as an immigration country. The designation as an immigration country means that net migration will remain positive and international migration is likely to affect population growth. KOSTAT (2011) used a model with more than 15 parameters to divide sexes, immigration and emigration based on the Wilson (2010) model, which takes into account population migration factors. Five years later, we assume the average of domestic net migration rate for the last five years and foreign government policy likely quota. However, both of these results were conservative estimates of international migration and provide different results than those used by the OECD and UN to classify an immigration country. In this paper, we proposed a stochastic projection on international migration using nonparametric model (FDM by Hyndman and Ullah (2007) and Coherent FDM by Hyndman et al. (2013)) that uses a functional data model for the international migration data of Korea from 2000-2017, noting the international migration such as immigration, emigration and net migration is non-linear and not linear. According to the result, immigration rate will be 1.098(male), 1.026(female) in 2018 and 1.228(male), 1.152(female) in 2025 per 1000 population, and the emigration rate will be 0.907(male), 0.879(female) in 2018 and 0.987(male), 0.959(female) in 2025 per 1000 population. Thus the net migration is expected to increase to 0.191(male), 0.148(female) in 2018 and 0.241(male), 0.192(female) in 2025 per 1000 population.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.769-777
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• 1998

In this study, various time series are analyzed to check nonlinearities of the data. The nonlinearity of a system can be investigated by testing the randomness of the time series data. To test the randomness, four nonparametric test statistics and a new test statistic, called the BDS statistic are used and the results and the results are compared. The Brock, Dechert, and Scheinkman (BDS) statistic is originated from the statistical properties of the correlation integral which is used for searching for chaos and has been shown very effective in distinguishing nonlinear structures in dynamic systems from random structures. As a result of application to linear and nonlinear models which are well known, the BDS statistic is found to be more effective than nonparametric test statistics in identifying nonlinear structure in the time series. Hydrologic time series data are fitted to ARMA type models and the statistics are applied to the residuals. The results show that the BDS statistic can distinguish chaotic nonlinearity from randomness and that the BDS statistic can also be used for verifying the validity of the fitted model.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• Journal of Venture Innovation
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• v.1 no.2
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• pp.35-46
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• 2018

The research on the efficiency analysis of domestic auto parts companies is mostly based on the calculation of the magnitude of the performance creation such as the sales amount compared to the input assets. However, the performance indicators such as sales, operating profit, and net profit are directly related to the cost structure and This cost structure is affected by changes in the firm's asset and capital structure. As a result, it is considered that efficient capital procurement needs to be done at the same time to create efficient management performance through proper investment. This study focuses on this point and attempts to measure the efficiency of procurement capital relative to the sales and other performance indicators generated by the first 33 suppliers who supply parts to Hyundai Kia Motors. Among the methods of evaluating efficiency, the DEA model based on the linear programming method is most widely used as a nonparametric method but The efficient frontier-based DEA model has the limitation that it can not use the variables that have a downward influence on the efficiency. This is inadequate to directly consider variables such as borrowings and total liabilities related to capital procurement. In this study, the efficiency of capital procurement was measured using Worst Practice DEA and the improvement direction of the capital procurement aspect of domestic auto parts companies was suggested

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.625-637
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• 2003

This study was performed to examine the relationship between air pollution exposure and mortality in Seoul for the years of 1998∼2001. Daily counts of death were analyzed by general additive Poisson model, with adjustment for effects of seasonal trend, air temperature, humidity, and day of the week as confounders in a nonparametric approach. Daily death counts were associated with CO (current day),O$_3$ (current day), PM$_{10}$ (current day), NO$_2$ (1 day before), SO$_2$ (1 day before). Increase of 41.71 $\mu\textrm{g}$/㎥ (interquartile range) in PM$_{10}$ was associated with 1.3% (95% CI = 0.7∼1.9％) increase in the daily number of death. $O_3$ concentrations resulted in an increased risk of 1.3% for 23.86 ppb in all-aged mortality [RR = 1.013 (1.004-1.023)1. This effect was greater in children (less than 15 aged) and elderly (more than 65 aged). After ozone level exceeds 25 ppb, the dose-response relationship between mortality and ozone was almost linear. We concluded that Seoul had 1∼5％ increase in mortality in association with IQR (interquartile range) in air pollutants. Daily variations in air pollution within the range currently occurring in Seoul might have an adverse effect on daily mortality. These findings also support the hypothesis that air pollution, at levels below the current ambient air quality standards of Korea, is harmful to sensitive subjects, such as children or elderly.rly.