Sensitivity Analysis in Auto- and Cross- Variogram Estimation

• 발행기관: 한국자료분석학회
• 저자명: Seung Bae Choi Kyu Kon Kim Yutaka Tanaka
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.2 No.1, 91~107쪽, 전체 17쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2000.03.30

영문 초록

Spatial, or geostatistical, data of a single variable are analyzed in three stages as follows: (1) estimating variograms, (2) fitting the variogram model to the estimated variograms, and (3) predicting the value at a specified location using the resulting fitted model. The final stage is called kriging . It is naturally extended to cokriging , which is performed in the similar three stages when multivariate data are available. In case additional observations of one and more covariables are available, cokriging may lead to increased precision of the prediction. Recently, sensitivity analysis for spatial data takes a growing interest in spatial statistics. The present article proposes a method to detect influential observations in the first stage of cokriging analysis. We derive influence functions, and then sensitivity analysis is performed using the derived influence functions. The Euclidean norms and principal component analysis(PCA) of the influence functions are used as methods to detect singly and jointly influential observations, respectively. A real numerical example is analyzed to show the validity or usefulness of the proposed influence functions.

목차

1. Introduction

2. Basic Conception of Sensitivity Analysis

3. Influence in Sample Auto- and Cross-Variograms

4. Numerical Example

5. Concluding Remarks

References