• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.105-114
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• 2006

In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.377-383
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• 2020

The likelihood-based inference in a nonlinear generalized mixed model often requires computing moments of truncated multivariate normal random variables. Many methods have been proposed for the computation using a recurrence relation or the moment generating function; however, these methods rely on high dimensional numerical integrations. The numerical method is known to be inefficient for high dimensional integral in accuracy. Besides the accuracy, the methods demand too much computing time to use them in practical analyses. In this note, a moment calculation method is proposed under an assumption of a certain covariance structure that occurred mostly in generalized mixed models. The method needs only low dimensional numerical integrations.

• 응용통계연구
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• 제22권5호
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• pp.1007-1017
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• 2009

본 연구는 최근에 활발히 연구가 진행 중인 항공수요 예측 분야에서 사용되는 계절형 ARIMA 모형과 다변량 계절형 시계열 모형과의 성능을 비교한 것이다. 본 연구에서는 국제 여객 수요와 국제 화물 수요 예측을 위하여 실제 자료를 이용하여 비교한 결과 다변량 계절형 시계열 모형이 예측의 정확도 면에서 기존의 일변량 모형보다 우수함을 보였다.

• 응용통계연구
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• 제30권1호
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• pp.169-180
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• 2017

본 논문은 다변량 변동성을 다루고 있다. 최근 들어 활발하게 연구가 되고 있는 고빈도(high frequency)자료에 기초한 변동성 측정방법인 실현변동성을 계산하고 기존의 다변량 GARCH 모형과 비교분석하였다. 정준상관분석과 VaR분석을 이용하여 실현변동성과 다양한 다변량 GARCH 모형을 비교하였으며 최근 6년 동안의 삼성전자/현대차 거래 가격 고빈도 데이터를 이용하여 실증분석을 실시하였다.

• 산업경영시스템학회지
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• 제42권1호
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• pp.106-114
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• 2019

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• 물과 미래
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• 제24권4호
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• pp.59-66
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• 1991

단기간의 실측자료를 이용하여 다변량 추계학적 모형에 의해 월유량 자료를 모의발생 시키는 목적은 수자원 시스템의 운영 조작 방침을 결정하기 위한 풍부한 입력자료를 제공하는데 있다. 본연구에서는 2종류의 다변량 모형(Thomas-Fiering 과 Matalas)을 서로 근접해 있는 두 지점에 적용하여 각각의 모형에 의한 모의 결과의 우수성과 적용가능성을 검토하여 보았으며, 이를 위해 모멘트법과 Fourier 분석에 의한 실측자료의 통계특성치를 구하였으며 비교의 기준으로는 실측치와 모의발생 자료의 통계특성을 이용하였다. 본 연구에 사용한 자료를 이용한 연구분석결과로는 다변량 Matalas 모형이 좀더 좋은 결과를 얻을 수 있었으며 변수추정도 수월함을 보였다.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.249-273
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• 1996

This paper reviews and develops several statistical models for extreme values, based on threshold methodology. Extreme values of a time series are modeled in terms of tails which are defined as truncated forms of original variables, and Markov property is imposed on the tails. Tails of the generalized extreme value distribution and a multivariate extreme value distributively, of the tails of the series. These models are then applied to real ozone data series collected in the Chicago area. A major concern is given to detecting any possible trend in the extreme values.

• Journal of the Korean Statistical Society
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• 제12권1호
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.