FINITE-SAMPLE BIAS OF THE QMLE IN SPATIAL AUTOREGRESSIVE MODELS

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results demonstrate that the feasible bias-correction procedure works remarkably well.

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FINITE SAMPLE BIAS OF THE QMLE IN SPATIAL AUTOREGRESSIVE MODELS – ERRATUM

Econometric Theory ◽

10.1017/s0266466612000424 ◽

2012 ◽

Vol 29 (1) ◽

pp. 89-89

Author(s):

Yong Bao

Keyword(s):

Autoregressive Models ◽

Finite Sample ◽

Sample Bias ◽

Spatial Autoregressive ◽

Spatial Autoregressive Models ◽

Finite Sample Bias

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Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2012.748913 ◽

2014 ◽

Vol 43 (10) ◽

pp. 2205-2212 ◽

Cited By ~ 5

Author(s):

Kevin Hayes

Keyword(s):

Bias Correction ◽

Median Absolute Deviation ◽

Correction Factors ◽

Finite Sample ◽

Absolute Deviation ◽

Sample Bias ◽

Finite Sample Bias

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Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Applied Mathematics and Nonlinear Sciences ◽

10.2478/amns.2021.2.00064 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Huiping Hu ◽

Xinqun Huang ◽

Majed Ahmad Suhaim ◽

Hui Zhang

Keyword(s):

Deep Learning ◽

Penalty Functions ◽

Autoregressive Models ◽

Model Parameters ◽

Small Range ◽

Campus Violence ◽

Security Measures ◽

Violent Crimes ◽

Spatial Autoregressive ◽

Spatial Autoregressive Models

Abstract To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.

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