• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.143-152
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• 2003

A data set having missing observations is often completed by using imputed values. In this paper, performances and accuracy of five imputation procedures are evaluated when missing values exist only on the response variable in the exponential regression model. Our simulation results show that adjusted exponential regression imputation procedure can be well used to compensate for missing data, in particular, compared to other imputation procedures. An illustrative example using real data is provided.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.135-144
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• 2003

A data set having missing observations is often completed by using imputed values. In this paper, performances and accuracy of five imputation procedures are evaluated when missing values exist only on the response variable in the exponential regression model. Our simulation results show that adjusted exponential regression imputation procedure can be well used to compensate for missing data, in particular, compared to other imputation procedures. An illustrative example using real data is provided.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.184-196
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• 2021

This paper proposes statistics adaptive linear regression-based object size prediction method for object detection. YOLOv2 and YOLOv3, which are typical deep learning-based object detection algorithms, designed the last layer of a network using statistics adaptive exponential regression model to predict the size of objects. However, an exponential regression model can propagate a high derivative of a loss function into all parameters in a network because of the property of an exponential function. We propose statistics adaptive linear regression layer to ease the gradient exploding problem of the exponential regression model. The proposed statistics adaptive linear regression model is used in the last layer of the network to predict the size of objects with statistics estimated from training dataset. We newly designed the network based on the YOLOv3tiny and it shows the higher performance compared to YOLOv3 tiny on the UFPR-ALPR dataset.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.531-550
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• 2023

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.39-43
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• 1999

We consider generalized exponential regression model with randomly censored data and propose a modified Fisher scoring method which estimates the model parameters. For this, the likelihood equations are derived and then the estimating algorithm is developed. We illustrate the proposed method using a real data.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.101-113
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• 2002

In this paper we introduce a new score generating (unction for the rank regression in the linear regression model. The score function compares the $\gamma$'th and s\`th power of the tail probabilities of the underlying probability distribution. We show that the rank estimate asymptotically converges to a multivariate normal. further we derive the asymptotic Pitman relative efficiencies and the most efficient values of $\gamma$ and s under the symmetric distribution such as uniform, normal, cauchy and double exponential distributions and the asymmetric distribution such as exponential and lognormal distributions respectively.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.321-332
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• 2013

Because of the continuous low birthrates, the number of the elementary students will decrease by 17% in 2020 compared to 2011. The purpose of this study is to forecast the number of elementary school teachers until 2020. We used the data in education statistical year books from 1970 to 2010. We used the time-series regression model, time series grouped regression model and exponential smoothing model to predict the number of teachers for the next ten years. Consequently time-series grouped regression model is a better model for forecasting the number of elementary school teachers than other models.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.179-188
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• 1996

Accelerated life testing(ALT) of products quickly yields information on life. In this paper, we investigate asymptotic normalities of maximum likelihood(ML) estimators of parameters for ALT model under Type II censored data using results of Bhattacharyya(1985). Further illustrations include the treatment of asymptotic of the exponential and Weibull regression models.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.195-210
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• 1999

We consider a large class of exponential regression models with censored data and propose two modified Fisher scoring methods with corresponding algorithms. These proposed methods improve the Newton-Raphson method in estimating the model parameters. The simulated and real examples are illustrated in aspect of convergence.