Comparison of Nelder Mead and BFGS Algorithms on Geographically Weighted Multivariate Negative Binomial

Yuliani .S. Dewi (1), - Purhadi (2), - Sutikno (3), Santi. W. Purnami (4)
(1) University of Jember
(2) Institut Teknologi Sepuluh Nopember
(3) Institut Teknologi Sepuluh Nopember
(4) Institut Teknologi Sepuluh Nopember
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How to cite (IJASEIT) :
Dewi, Yuliani .S., et al. “Comparison of Nelder Mead and BFGS Algorithms on Geographically Weighted Multivariate Negative Binomial”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 3, May 2019, pp. 979-87, doi:10.18517/ijaseit.9.3.6932.
Geographically Weighted Negative Binomial Regression (GWNBR) was proposed related to univariate spatial count data with overdispersion using MLE via Newton Raphson algorithm. However, the Newton Raphson algorithm has the weakness, it tends to depend on the initial value. Therefore, it can have false convergence if the initial value is mistaken. In this research, we derive estimating the mean of dependent variables of multivariate spatial count data with overdispersion, Geographically Weighted Multivariate Negative Binomial (GWMNB) and compare it to the global method, multivariate negative binomial (MNB). We use MLE via Nelder Mead and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms. We conduct the simulation study and application of mortality data to find out the characteristics of the methods. They show that GWMNB performs better than global method (MNB) in estimating the means of dependent variables of the spatial data. The Nelder Mead tends to be more successful in estimating the means for all locations than BFGS algorithm. Although BFGS is a stable algorithm in MNB related to the initial value, it tends to have false convergence in GWMNB. The mortality rate of infant is larger than it of toddler and preschool and also maternal. The highest deaths of infant, toddler, and preschool and also maternal tend to happen in east parts of East Java.

S. Gurmu and P. K. Trivedi, Recent developments in models of event counts: a survey. University of Virginia, Thomas Jefferson Center for Political Economy, 1994.

A. C. Cameron and P. K. Trivedi, Regression analysis of count data, vol. 53. Cambridge university press, 2013.

R. E. Society and T. E. Journal, “Simulated maximum likelihood estimation of multivariate mixed-Poisson regression models, with application Author (s): Murat K. Munkin and Pravin K. Trivedi Published by : Wiley on behalf of the Royal Economic Society Stable URL : http://www.jstor.org,” vol. 2, no. 1, pp. 29-48, 2017.

D. Karlis and I. Ntzoufras, “Bivariate Poisson and Diagonal Inflated Bivariate Poissn Regression Models in R,” J. Stat. Softw., vol. 30, no. April, pp. 1-3, 2009.

H. Zamani, P. Faroughi, and N. Ismail, “Bivariate generalized Poisson regression model : applications on health care data,” Empir. Econ., 2016.

D. Karlis and L. Meligkotsidou, “Multivariate Poisson regression with covariance structure,” Stat. Comput., vol. 15, no. 4, pp. 255-265, 2005.

F. Famoye, “A multivariate generalized poisson regression model,” Commun. Stat. - Theory Methods, vol. 44, no. 3, pp. 497-511, 2015.

F. Famoye, “On the bivariate negative binomial regression model,” J. Appl. Stat., vol. 37, no. 6, pp. 969-981, 2010.

Y. S. Dewi, Purhadi, Sutikno and S. W. Purnami, “Comparison of Bivariate Negative Binomial Regression Models for Handling Over dispersion,” Int. J. Appl. Math. Stat., vol. 56, no. 5, pp. 53-62, 2017.

R. Winkelman, “Seemingly unrelated negative binomial regression,” Oxford Bull. Econ. Stat., vol. 62, no. 4, pp. 553-560, 2000.

P. Shi and E. A. Valdez, “Multivariate negative binomial models for insurance claim counts,” Insur. Math. Econ., vol. 55, no. 1, pp. 18-29, 2014.

I. L. Solis”Trapala and V. T. Farewell, “Regression analysis of overdispersed correlated count data with subject specific covariates,” Stat. Med., vol. 24, no. 16, pp. 2557-2575, 2005.

L. Anselin, “Spatial econometrics,” A companion to Theor. Econom., pp. 310-330, 2001.

A. R. da Silva and T. C. V. Rodrigues, “Geographically Weighted Negative Binomial Regression-incorporating overdispersion,” Stat. Comput., vol. 24, no. 5, pp. 769-783, 2014.

Purhadi, Y. S. Dewi, and L. Amaliana, “Zero Inflated Poisson and Geographically Weighted Zero- Inflated Poisson Regression Model: Application to Elephantiasis (Filariasis) Counts Data,” J. Math. Stat., vol. 11, no. 2, pp. 52-60, 2015.

J. Nocedal and S. Wright, Numerical Optimization. Springer New York, 2006.

J. C. Nash, Nonlinear Parameter Optimization Using R Tools. John Wiley and Sons Ltd, 2014.

P. Harris, A. S. Fotheringham, and S. Juggins, “Robust Geographically Weighted Regression : A,” Ann. Assoc. Am. Geogr., vol. 100, no. December 2014, pp. 37-41, 2010.

A. Pí¡ez, S. Farber, and D. Wheeler, “A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships,” Environ. Plan. A, vol. 43, no. 12, pp. 2992-3010, 2011.

R. Winkelmann, Econometric Analysis of Count Data, 5th ed. Springer Publishing Company, Incorporated, 2008.

A. S. Fotheringham, C. Brunsdon, and M. Charlton, “Geographically Weighted Regression-The Analysis of Spatially Varying Relationships.” Chichester, UK: John Wiley & Sons, 2002.

D.-H. Li and M. Fukushima, “On the Global Convergence of the BFGS Method for Nonconvex Unconstrained Optimization Problems,” SIAM J. Optim., vol. 11, no. 4, pp. 1054-1064, 2001.

J. F. Lawless, “Negative binomial and mixed poisson regression,” Can. J. Stat., vol. 15, no. 3, pp. 209-225, 1987.

C. G. Broyden, “A class of methods for solving nonlinear simultaneous equations,” Math. Comput., vol. 19, no. 92, pp. 577-593, 1965.

J. C. Nash, Compact numerical methods for computers: linear algebra and function minimisation. CRC press, 1990.

J. C. Nash, “On best practice optimization methods in R,” J. Stat. Softw., vol. 60, no. 2, pp. 1-14, 2014.

J. A. Nelder and R. Mead, “A simplex method for function minimization,” Comput. J., vol. 7, no. 4, pp. 308-313, 1965.

C. G. Small and J. Wang, Numerical methods for nonlinear estimating equations, vol. 29. Oxford University Press on Demand, 2003.

J. M. Hilbe, Negative Binomial Regression, Second. Cambridge: Cambridge University Press, 2011.

Dinas Kesehatan Provinsi Jawa Timur, “Profil Kesehatan Provinsi Jawa Timur 2014,” 2015.

Bappenas, Faktor-faktor yang Mempengaruhi Kelangsungan Hidup Anak. Badan Perencanaan Pembangunan Daerah, 2009.

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