• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.479-490
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• 2006

Francis Gallon (1822-1911) introduced the term 'regression' and 'correlation' in the study on human inheritance of the stature from parents to their children. In almost every statistics textbook, superficial attentions have been given to him just as the inventor of the term 'regression'. Rereading his books and papers, we investigated problems he had tried to solve and the methods he had used to solve the problems. In addition, we tried to find the motivation that had led Gallon to take attention to the variation rather than the central tendency of observational data that had fascinated his forerunner Adloph Quetelet.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.61-69
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• 2011

In GARCH context, the conditional variance (or volatility) is of a quadratic function of the observation process. Examine standard ARCH/GARCH and their variant models in terms of quadratic formulations and it is interesting to note that most models in GARCH context have contained neither the first order term nor the interaction term. In this paper, we consider three models possessing the first order and/or interaction terms in the formulation of conditional variances, viz., quadratic GARCH, absolute value GARCH and bilinear GARCH processes. These models are investigated with a view to model comparisons and applications to financial time series in Korea

• Bulletin of the Korean Mathematical Society
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• v.52 no.5
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• pp.1495-1515
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• 2015

This paper deals with the numerical methods for the reconstruction of the source term in a linear parabolic equation from final overdetermination. We assume that the source term has the form f(x)h(t) and h(t) is given, which guarantees the uniqueness of the inverse problem of determining the source term f(x) from final overdetermination. We present the regularization methods for reconstruction of the source term in the whole real line and with Neumann boundary conditions. Moreover, we show the connection of the solutions between the problem with Neumann boundary conditions and the problem with no boundary conditions (on the whole real line) by using the extension method. Numerical experiments are done for the inverse problem with the boundary conditions.

• Asian Journal of Innovation and Policy
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• v.10 no.3
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• pp.330-354
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• 2021

To know the long-term growth patterns and determinants of successful startups, 15-year (2006-2020) panel data of 252 companies that had a growth rate of over 20% every year in the last three years were used. In the first analysis, statistics on the period required to designate a gazelle company or listed on the stock market were examined. In addition, five long-term growth patterns were presented. In the panel analysis, the R&D intensity, operating profit ratio, size, and age of the company were pointed out as determinants of growth. The operating profit margin and R&D intensity have a positive effect on growth. Gibrat's law was not supported, but an inverted U-shape was observed. Jovanovic's law was confirmed. Although many studies tend not to point to profitability as a determinant of long-term growth, this is an important long-term growth factor of a company. The operating profit ratio was used in this study.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.253-265
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• 2013

It has been interested in developing useful information variables that are able to predict the future movement of final objects to attain the specific policy and strategic target. Term structure of interest rates is known as an important variable to predict future business and economic activity, yet there is little empirical work on the predictability of future changes in real output. This study attempts to develop the statistical model and examine whether domestic term structure of interest rates can predict variations of future cumulative changes in real growth on a long time horizon.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.623-630
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• 2003

Recently, Tanaka(1988) derived two influence functions related to an eigenvalue problem $(A-\lambda_sI)\upsilon_s=0$ of real symmetric matrix A and used them for sensitivity analysis in principal component analysis. In this paper, we deal with the perturbation expansions up to quadratic terms of the same functions and discuss the application to sensitivity analysis in principal component regression analysis(PCRA). Numerical example is given to show how the approximation improves with the quadratic term.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.200-208
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• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.