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CSAM(Communications for Statistical Applications and Methods) update

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수록범위 : 1권1호(1994)~26권3호(2019) |수록논문 수 : 1,851
CSAM(Communications for Statistical Applications and Methods)
26권3호(2019년 05월) 수록논문
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1The transmuted GEV distribution: properties and application

저자 : Cira E. G. Otiniano , Bianca S. De Paiva , Daniele S. B. Martins Netob

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 239-259 (21 pages)

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The transmuted generalized extreme value (TGEV) distribution was first introduced by Aryal and Tsokos (Nonlinear Analysis: Theory, Methods & Applications, 71, 401-407, 2009) and applied by Nascimento et al. (Hacettepe Journal of Mathematics and Statistics, 45, 1847-1864, 2016). However, they did not give explicit expressions for all the moments, tail behaviour, quantiles, survival and risk functions and order statistics. The TGEV distribution is a more flexible model than the simple GEV distribution to model extreme or rare events because the right tail of the TGEV is heavier than the GEV. In addition the TGEV distribution can adjusted various forms of asymmetry. In this article, explicit expressions for these measures of the TGEV are obtained. The tail behavior and the survival and risk functions were determined for positive gamma, the moments for nonzero gamma and the moment generating function for zero gamma. The performance of the maximum likelihood estimators (MLEs) of the TGEV parameters were tested through a series of Monte Carlo simulation experiments. In addition, the model was used to fit three real data sets related to financial returns.

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2Interval prediction on the sum of binary random variables indexed by a graph

저자 : Seongoh Parka , Kyu S. Hahnb , Johan Lima , Won Son

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 261-272 (12 pages)

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In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the 19th and 20th Korea National Assembly elections.

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3Least absolute deviation estimator based consistent model selection in regression

저자 : K. S. Shende , D. N. Kashida

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 273-293 (21 pages)

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We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

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4Threshold-asymmetric volatility models for integer-valued time series

저자 : Deok Ryun Kim , Jae Eun Yoon , Sun Young Hwang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 295-304 (10 pages)

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This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

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5Higher-order solutions for generalized canonical correlation analysis

저자 : Hyuncheol Kang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 305-313 (9 pages)

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Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.

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6A computational note on maximum likelihood estimation in random effects panel probit model

저자 : Seung-chun Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 315-323 (9 pages)

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Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

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7Application of discrete Weibull regression model with multiple imputation

저자 : Hanna Yoo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 3호 발행 연도 : 2019 페이지 : pp. 325-336 (12 pages)

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In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

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