doi: 10.3934/mfc.2020012

Aims: Average information matrix splitting

1. 

Laboratory for Intelligent Computing and Financial Technology, Department of Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China

2. 

Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China

* Corresponding author: Shengxin Zhu

Received  November 2019 Revised  February 2020 Published  June 2020

Fund Project: This research is supported by Foundation of LCP(6142A05180501), Jiangsu Science and Technology Basic Research Program (BK20171237), Key Program Special Fund of XJTLU (KSF-E-21, KSF-P-02), Research Development Fund of XJTLU (RDF-2017-02-23) and partially supported by NSFC (No.11771002, 11571047, 11671049, 11671051, 6162003, and 11871339)

For linear mixed models with co-variance matrices which are not linearly dependent on variance component parameters, we prove that the average of the observed information and the Fisher information can be split into two parts. The essential part enjoys a simple and computational friendly formula, while the other part which involves a lot of computations is a random zero matrix and thus is negligible.

Citation: Shengxin Zhu, Tongxiang Gu, Xingping Liu. Aims: Average information matrix splitting. Mathematical Foundations of Computing, doi: 10.3934/mfc.2020012
References:
[1]

Z. Chen, S. Zhu, Q. Niu and X. Lu, Censorious young: Knowledge discovery from high-throughput movie rating data with LME4, in 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019, 32–36. doi: 10.1109/ICBDA.2019.8713193.  Google Scholar

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Z. ChenS. ZhuQ. Niu and T. Zuo, Knowledge discovery and recommendation with linear mixed model, IEEE Access, 8 (2020), 38304-38317.  doi: 10.1109/ACCESS.2020.2973170.  Google Scholar

[3]

B. Efron and D. V. Hinkley, Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information, Biometrika, 65 (1978), 457-483.  doi: 10.1093/biomet/65.3.457.  Google Scholar

[4]

R. A. Fisher, The Genetical Theory of Natural Selection, Oxford University Press, Oxford, 1999.  Google Scholar

[5]

B. Gao, G. Zhan, H. Wang, Y. Wang and S. Zhu, Learning with linear mixed model for group recommendation systems, in Proceedings of the 2019 11th International Conference on Machine Learning and Computing, ICMLC '19, Association for Computing Machinery, New York, NY, 2019, 81–85. doi: 10.1145/3318299.3318342.  Google Scholar

[6]

A. R. GilmourR. Thompson and B. R. Cullis, Average information reml: An efficient algorithm for variance parameter estimation in linear mixed models, Biometrics, 51 (1995), 1440-1450.  doi: 10.2307/2533274.  Google Scholar

[7]

G. Givens and J. Hoeting, Computational Statistics, 2$^{nd}$ edition, Wiley Series in Computation Statistics, John Wiley & Sons, Inc., Wiley, NJ, 2005.  Google Scholar

[8]

F. N. Gumedze and T. T. Dunne, Parameter estimation and inference in the linear mixed model, Linear Algebra Appl., 435 (2011), 1920-1944.  doi: 10.1016/j.laa.2011.04.015.  Google Scholar

[9]

A. Heavens, Generalised Fisher matrices, Entropy, 18 (2016), 8 pp. doi: 10.3390/e18060236.  Google Scholar

[10]

W. JankeD. Johnston and R. Kenna, Information geometry and phase transitions, Physica A: Statistical Mechanics and its Applications, 336 (2004), 181-186.  doi: 10.1016/j.physa.2004.01.023.  Google Scholar

[11]

R. I. Jennrich and P. F. Sampson, Newton-Raphson and related algorithms for maximum likelihood variance component estimation, Technometrics, 18 (1976), 11-17.  doi: 10.2307/1267911.  Google Scholar

[12]

D. Johnson and R. Thompson, Restricted maximum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information, Journal of Dairy Science, 78 (1995), 449-456.  doi: 10.3168/jds.S0022-0302(95)76654-1.  Google Scholar

[13]

N. T. Longford, A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects, Biometrika, 74 (1987), 817-827.  doi: 10.1093/biomet/74.4.817.  Google Scholar

[14]

K. Meyer, An average information restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices, Genetics Selection Evolution, 29 (1997), 97. doi: 10.1186/1297-9686-29-2-97.  Google Scholar

[15]

J. I. Myung and D. J. Navarro, Information Matrix, American Cancer Society, 2005. doi: 10.1002/0470013192.bsa302.  Google Scholar

[16]

J. W. Pratt, F. Y. Edgeworth and R. A. Fisher on the efficiency of maximum likelihood estimation, Ann. Statist., 4 (1976), 501-514.  doi: 10.1214/aos/1176343457.  Google Scholar

[17]

M. Prokopenko, J. T. Lizier, O. Obst and X. R. Wang, Relating Fisher information to order parameters, Phys. Rev. E, 84 (2011), 041116. doi: 10.1103/PhysRevE.84.041116.  Google Scholar

[18]

S. R. Searle, G. Casella and C. E. McCulloch, Variance Components, Wiley Series in Probability and Statistics, Wiley-Interscience [John Wiley & Sons], Hoboken, NJ, 2006.  Google Scholar

[19]

M. Vallisneri, A User Manual for the Fisher Informaiton Matrix, California Institute of Technology, Jet Propulsion Laboratory, 2007. Google Scholar

[20]

R. S. Varga, Matrix Iterative Analysis, expanded edition, Springer Series in Computational Mathematics, 27, Springer-Verlag, Berlin, 2000. doi: 10.1007/978-3-642-05156-2.  Google Scholar

[21]

Y. Wang, T. Wu, F. Ma and S. Zhu, Personalized recommender systems with multiple source data, in Computing Conference 2020 Google Scholar

[22]

S. Welham, S. Zhu and A. J. Wathen, Big Data, Fast Models: Faster Calculation of Models from High-Throughput Biological Data Sets, Knowledge Transfer Report IP12-0009, Smith Institute and The Universtiy of Oxford, Oxford, 2013. Google Scholar

[23]

R. Zamir, A Necessary and Sufficient Condition for Equality in the Matrix Fisher Information Inequality, Technical report, Tel Aviv University, 1997.  Google Scholar

[24]

R. Zamir, A proof of the Fisher information inequality via a data processing argument, IEEE Transactions on Information Theory, 44 (1998), 1246-1250.  doi: 10.1109/18.669301.  Google Scholar

[25]

S. Zhu, T. Gu and X. Liu, Information matrix splitting, preprint, arXiv: 1605.07646. Google Scholar

[26]

S. Zhu and A. J. Wathen, Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models, preprint, arXiv: 1805.05188. Google Scholar

[27]

S. Zhu and A. J. Wathen, Sparse inversion for derivative of log determinant, arXiv: 1911.00685. Google Scholar

[28]

T. Zuo, S. Zhu and J. Lu, A hybrid recommender system combing singular value decomposition and linear mixed model, in Computing Conference 2020, Advance in Intelligent Systems and Computing, Springer International Publishing, Cham, 2020. Google Scholar

show all references

References:
[1]

Z. Chen, S. Zhu, Q. Niu and X. Lu, Censorious young: Knowledge discovery from high-throughput movie rating data with LME4, in 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019, 32–36. doi: 10.1109/ICBDA.2019.8713193.  Google Scholar

[2]

Z. ChenS. ZhuQ. Niu and T. Zuo, Knowledge discovery and recommendation with linear mixed model, IEEE Access, 8 (2020), 38304-38317.  doi: 10.1109/ACCESS.2020.2973170.  Google Scholar

[3]

B. Efron and D. V. Hinkley, Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information, Biometrika, 65 (1978), 457-483.  doi: 10.1093/biomet/65.3.457.  Google Scholar

[4]

R. A. Fisher, The Genetical Theory of Natural Selection, Oxford University Press, Oxford, 1999.  Google Scholar

[5]

B. Gao, G. Zhan, H. Wang, Y. Wang and S. Zhu, Learning with linear mixed model for group recommendation systems, in Proceedings of the 2019 11th International Conference on Machine Learning and Computing, ICMLC '19, Association for Computing Machinery, New York, NY, 2019, 81–85. doi: 10.1145/3318299.3318342.  Google Scholar

[6]

A. R. GilmourR. Thompson and B. R. Cullis, Average information reml: An efficient algorithm for variance parameter estimation in linear mixed models, Biometrics, 51 (1995), 1440-1450.  doi: 10.2307/2533274.  Google Scholar

[7]

G. Givens and J. Hoeting, Computational Statistics, 2$^{nd}$ edition, Wiley Series in Computation Statistics, John Wiley & Sons, Inc., Wiley, NJ, 2005.  Google Scholar

[8]

F. N. Gumedze and T. T. Dunne, Parameter estimation and inference in the linear mixed model, Linear Algebra Appl., 435 (2011), 1920-1944.  doi: 10.1016/j.laa.2011.04.015.  Google Scholar

[9]

A. Heavens, Generalised Fisher matrices, Entropy, 18 (2016), 8 pp. doi: 10.3390/e18060236.  Google Scholar

[10]

W. JankeD. Johnston and R. Kenna, Information geometry and phase transitions, Physica A: Statistical Mechanics and its Applications, 336 (2004), 181-186.  doi: 10.1016/j.physa.2004.01.023.  Google Scholar

[11]

R. I. Jennrich and P. F. Sampson, Newton-Raphson and related algorithms for maximum likelihood variance component estimation, Technometrics, 18 (1976), 11-17.  doi: 10.2307/1267911.  Google Scholar

[12]

D. Johnson and R. Thompson, Restricted maximum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information, Journal of Dairy Science, 78 (1995), 449-456.  doi: 10.3168/jds.S0022-0302(95)76654-1.  Google Scholar

[13]

N. T. Longford, A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects, Biometrika, 74 (1987), 817-827.  doi: 10.1093/biomet/74.4.817.  Google Scholar

[14]

K. Meyer, An average information restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices, Genetics Selection Evolution, 29 (1997), 97. doi: 10.1186/1297-9686-29-2-97.  Google Scholar

[15]

J. I. Myung and D. J. Navarro, Information Matrix, American Cancer Society, 2005. doi: 10.1002/0470013192.bsa302.  Google Scholar

[16]

J. W. Pratt, F. Y. Edgeworth and R. A. Fisher on the efficiency of maximum likelihood estimation, Ann. Statist., 4 (1976), 501-514.  doi: 10.1214/aos/1176343457.  Google Scholar

[17]

M. Prokopenko, J. T. Lizier, O. Obst and X. R. Wang, Relating Fisher information to order parameters, Phys. Rev. E, 84 (2011), 041116. doi: 10.1103/PhysRevE.84.041116.  Google Scholar

[18]

S. R. Searle, G. Casella and C. E. McCulloch, Variance Components, Wiley Series in Probability and Statistics, Wiley-Interscience [John Wiley & Sons], Hoboken, NJ, 2006.  Google Scholar

[19]

M. Vallisneri, A User Manual for the Fisher Informaiton Matrix, California Institute of Technology, Jet Propulsion Laboratory, 2007. Google Scholar

[20]

R. S. Varga, Matrix Iterative Analysis, expanded edition, Springer Series in Computational Mathematics, 27, Springer-Verlag, Berlin, 2000. doi: 10.1007/978-3-642-05156-2.  Google Scholar

[21]

Y. Wang, T. Wu, F. Ma and S. Zhu, Personalized recommender systems with multiple source data, in Computing Conference 2020 Google Scholar

[22]

S. Welham, S. Zhu and A. J. Wathen, Big Data, Fast Models: Faster Calculation of Models from High-Throughput Biological Data Sets, Knowledge Transfer Report IP12-0009, Smith Institute and The Universtiy of Oxford, Oxford, 2013. Google Scholar

[23]

R. Zamir, A Necessary and Sufficient Condition for Equality in the Matrix Fisher Information Inequality, Technical report, Tel Aviv University, 1997.  Google Scholar

[24]

R. Zamir, A proof of the Fisher information inequality via a data processing argument, IEEE Transactions on Information Theory, 44 (1998), 1246-1250.  doi: 10.1109/18.669301.  Google Scholar

[25]

S. Zhu, T. Gu and X. Liu, Information matrix splitting, preprint, arXiv: 1605.07646. Google Scholar

[26]

S. Zhu and A. J. Wathen, Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models, preprint, arXiv: 1805.05188. Google Scholar

[27]

S. Zhu and A. J. Wathen, Sparse inversion for derivative of log determinant, arXiv: 1911.00685. Google Scholar

[28]

T. Zuo, S. Zhu and J. Lu, A hybrid recommender system combing singular value decomposition and linear mixed model, in Computing Conference 2020, Advance in Intelligent Systems and Computing, Springer International Publishing, Cham, 2020. Google Scholar

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