• 응용통계연구
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• 제28권2호
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• pp.353-360
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• 2015

공간적으로 관측되는 연속형 자료를 분석하는 모형으로 공간적 상관관계를 고려한 다양한 정규모형이 지난 수십 년간 제안되었다. 그 중에서 공간효과를 랜덤효과로 모형화하는 공간선형모형(Spatial Linear Mixed Model; SLMM)이 가장 널리 활용되는 모형 중 하나일 것이다. 연결함수(link function)을 사용하면 SLMM을 비정규 데이터도 적용할 수 있는 일반화된 공간선형모형(Spatial Generalized Linear Mixed Model; SGLMM)으로 자연스럽게 확장할 수 있다. 이 논문에서는 가장 널리 활용되는 SGLMM을 알아보고 실제 데이터 적용사례를 R 패키지를 활용하여 제시하고자 한다.

• Journal of the Korean Statistical Society
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• 제29권2호
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• pp.231-245
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• 2000

This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.

• 품질경영학회지
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• 제34권2호
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• pp.122-128
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• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1215-1224
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• 2016

고해상도 격자 단위 기후정보는 농업, 관광학, 생태학, 질병학 등 다양한 분야의 현상을 설명하는 중요 요인이다. 고해상도 기후정보는 동적 모형과 통계적 모형을 통해 얻을 수 있다. 통계적 모형은 동적 모형에 비해 계산 시간이 저렴하여 시공간 해상도가 높은 기후자료 생성에 주로 이용한다. 본 연구에서는 2003년부터 2012년까지 1월에 관측된 일 평균기온자료를 토대로 통계적 모형의 일 평균 기온을 생성하였다. 통계적 모형으로 선형모형을 기반으로한 일반선형모형, 일반화가법모형, 공간선형모형, 베이지안공간선형모형을 고려하였다. 예측성능평가를 위해 60개소의 지상관측소에서 관측된 일 평균기온을 모형적합 자료로 사용하여 352개소의 자동기상관측의 일 평균기온을 검증하였다. 평균제곱오차와 상관계수를 보면 베이지안공간모형의 예측성능이 다른 모형에 비해 상대적으로 우수하였다. 최종적으로 $1km{\times}1km$ 격자 단위 일 평균기온 지도를 생성하였다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 추계학술대회논문집
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• pp.23-29
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• 2003

Detection in remotely sensed images can be conducted spatially, spectrally or both [2]. If the images have high spatial resolution, materials can be detected by using spatial and spectral information, unless we can't see the object embedded in a pixel. In this paper, we intend to solve the limit of spatial resolution by using the hyperspectral image which has high spectral resolution. Therefore, the Linear Spectral Mixing(LSM) Model which is sub-pixel detection algorithm is used to solve this problem. To find class Endmembers, we applied Geometric Model with MNF(Minimum Noise Fraction) transformation. From the result of sub-pixel detection algorithm, we can see the detection of water is satisfied and the object shape cannot be extracted but the possibility of material existence can be identified.

• 한국환경과학회지
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• 제20권12호
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• 품질경영학회지
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• 제24권1호
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• pp.137-145
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• 1996

We propose a model which is useful for the analysis of the spatial time series data. The proposed model utilized the linear dependences across the spatial units as well as over time. Three stage model fitting procedures are suggested and the real data is analyzed.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.296-302
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• 1995

When heteroscedasticity occurs in random effects linear model, the error variance may depend on the values of one or more of the explanatory variables or on other relevant quantities such as time or spatial ordering. In this paper we derive a score test as a diagnostic tool for detecting non-constant error variance in random effefts linear model based on the model expansion on error variance. This score test is compared to loglikelihood ratio test.