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한국통계학회> CSAM(Communications for Statistical Applications and Methods)

CSAM(Communications for Statistical Applications and Methods) update

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  • : 한국통계학회논문집(~2011)→Communications for statistical applications and methods(2012~)

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수록범위 : 1권1호(1994)~26권6호(2019) |수록논문 수 : 1,873
CSAM(Communications for Statistical Applications and Methods)
26권6호(2019년 11월) 수록논문
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KCI등재

1Optimal designs for small Poisson regression experiments using second-order asymptotic

저자 : S. Mehr Mansour , M. Niaparast

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 527-538 (12 pages)

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This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

KCI등재

2On study for change point regression problems using a difference-based regression model

저자 : Jong Suk Park , Chun Gun Park , Kyeong Eun Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 539-556 (18 pages)

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This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, “no matter what regression lines” and “no matter what the number of change points”.

KCI등재

3Analysis of cause-of-death mortality and actuarial implications

저자 : Hyuk-sung Kwon , Vu Hai Nguyen

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 557-573 (17 pages)

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Mortality study is an essential component of actuarial risk management for life insurance policies, annuities, and pension plans. Life expectancy has drastically increased over the last several decades; consequently, longevity risk associated with annuity products and pension systems has emerged as a crucial issue. Among the various aspects of mortality study, a consideration of the cause-of-death mortality can provide a more comprehensive understanding of the nature of mortality/longevity risk. In this case study, the cause-of-mortality data in Korea and the US were analyzed along with a multinomial logistic regression model that was constructed to quantify the impact of mortality reduction in a specific cause on actuarial values. The results of analyses imply that mortality improvement due to a specific cause should be carefully monitored and reflected in mortality/longevity risk management. It was also confirmed that multinomial logistic regression model is a useful tool for analyzing cause-of-death mortality for actuarial applications.

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4Unified methods for variable selection and outlier detection in a linear regression

저자 : Han Son Seo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 575-582 (8 pages)

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The problem of selecting variables in the presence of outliers is considered. Variable selection and outlier detection are not separable problems because each observation affects the fitted regression equation differently and has a different influence on each variable. We suggest a simultaneous method for variable selection and outlier detection in a linear regression model. The suggested procedure uses a sequential method to detect outliers and uses all possible subset regressions for model selections. A simplified version of the procedure is also proposed to reduce the computational burden. The procedures are compared to other variable selection methods using real data sets known to contain outliers. Examples show that the proposed procedures are effective and superior to robust algorithms in selecting the best model.

KCI등재

5Some counterexamples of a skew-normal distribution

저자 : Jun Zhao , Sang Kyu Lee , Hyoung-moon Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 583-589 (7 pages)

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Counterexamples of a skew-normal distribution are developed to improve our understanding of this distribution. Two examples on bivariate non-skew-normal distribution owning marginal skew-normal distributions are first provided. Sum of dependent skew-normal and normal variables does not follow a skew-normal distribution. Continuous bivariate density with discontinuous marginal density also exists in skew-normal distribution. An example presents that the range of possible correlations for bivariate skew-normal distribution is constrained in a relatively small set. For unified skew-normal variables, an example about converging in law are discussed. Convergence in distribution is involved in two separate examples for skew-normal variables. The point estimation problem, which is not a counterexample, is provided because of its importance in understanding the skew-normal distribution. These materials are useful for undergraduate and/or graduate teaching courses.

KCI등재

6Introduction to convolutional neural network using Keras; an understanding from a statistician

저자 : Hagyeong Lee , Jongwoo Song

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 591-610 (20 pages)

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Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

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7Estimation of long memory parameter in nonparametric regression

저자 : Yeoyoung Cho , Changryong Baek

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 611-622 (12 pages)

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This paper considers the estimation of the long memory parameter in nonparametric regression with strongly correlated errors. The key idea is to minimize a unified mean squared error of long memory parameter to select both kernel bandwidth and the number of frequencies used in exact local Whittle estimation. A unified mean squared error framework is more natural because it provides both goodness of fit and measure of strong dependence. The block bootstrap is applied to evaluate the mean squared error. Finite sample performance using Monte Carlo simulations shows the closest performance to the oracle. The proposed method outperforms existing methods especially when dependency and sample size increase. The proposed method is also illustreated to the volatility of exchange rate between Korean Won for US dollar.

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8On the comparison of cumulative hazard functions

저자 : Sangun Park , Seung Ah Ha

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 623-633 (11 pages)

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This paper proposes two distance measures between two cumulative hazard functions that can be obtained by comparing their difference and ratio, respectively. Then we estimate the measures and present goodness of t test statistics. Since the proposed test statistics are expressed in terms of the cumulative hazard functions, we can easily give more weights on earlier (or later) departures in cumulative hazards if we like to place an emphasis on earlier (or later) departures. We also show that these test statistics present comparable performances with other well-known test statistics based on the empirical distribution function for an exponential null distribution. The proposed test statistic is an omnibus test which is applicable to other lots of distributions than an exponential distribution.

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9A model-free soft classification with a functional predictor

저자 : Eugene Lee , Seung Jun Shin

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 6호 발행 연도 : 2019 페이지 : pp. 635-644 (10 pages)

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Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

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