JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Generalized Maximum Entropy Estimator for the Linear Regression Model with a Spatial Autoregressive Disturbance
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Generalized Maximum Entropy Estimator for the Linear Regression Model with a Spatial Autoregressive Disturbance
Cheon, Soo-Young; Lim, Seong-Seop;
  PDF(new window)
 Abstract
This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.
 Keywords
Spatial linear regression model;information recovery;GME estimation;
 Language
Korean
 Cited by
1.
Application of Generalized Maximum Entropy Estimator to the Two-way Nested Error Component Model with III-Posed Data,;

Communications for Statistical Applications and Methods, 2009. vol.16. 4, pp.659-667 crossref(new window)
2.
공간 격자데이터 분석에 대한 우위성 비교 연구 - 이상치가 존재하는 경우 -,김수정;최승배;강창완;조장식;

Communications for Statistical Applications and Methods, 2010. vol.17. 2, pp.193-204 crossref(new window)
1.
A Comparative Study on Spatial Lattice Data Analysis - A Case Where Outlier Exists -, Communications for Statistical Applications and Methods, 2010, 17, 2, 193  crossref(new windwow)
 References
1.
송석헌, 전수영 (2006). 패널회귀모형에서 최대엔트로피 추정량에 관한 연구, <응용통계연구>, 19, 521-534

2.
이재준 (2002). 오차항이 공간자기상관을 갖는 선형회귀모형에서 회귀계수 검정에 관한 연구, <고려대학교 석사학위 논문>

3.
Anselin, L. and Bera, A. (1998). Spatial Dependence in Linear Regression Model with an Introduction to Spatial Econometrics, Handbook of Applied Economic Statistics, New York

4.
Anselin, L. (2002). Under the hood: Issues in the specification and interpretation of spatial regression models, Agricultural Economics, 27, 247-267 crossref(new window)

5.
Belsley. D. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression, John Wiley & Sons, New York

6.
Cliff, A. D. and Ord, J. K. (1973). Spatial Autocorrelation, Pion, London

7.
Dubin, R. A. (1998). Spatial autocorrelation: A primer, Journal of Housing Economics, 7, 304-327 crossref(new window)

8.
Golan, A. (1994). A multi-variable stochastic theory of size distribution of firms with empirical evidence, Advances in Econometrics, 10, 1-46

9.
Griffith, D. A. (1988). Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series, Kluwer Academic Publishers

10.
Judge, G. G. and Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise, Unpublished paper, University of California at Berkeley

11.
Kelejian, H. H. and Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances, The Journal of Real Estate Finance and Economics, 17, 99-121 crossref(new window)

12.
Kelejian, H. H. and Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model, International Economic Review, 40, 509-533 crossref(new window)

13.
Moulton, B. R. (1986). Random group effects and the precision of regression estimates, Journal of Econo-metrics, 32, 385-397 crossref(new window)

14.
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region, Economic Geography, 46, 230-240