### Using the Generalized Partial Linear Regression Model to Determine Climatic Factor Effect on Dust Storms in Baghdad Governorate

#### Abstract

The phenomenon of dust that occurs in Iraq is one of the phenomena that cannot be completely controlled or partially processed in a short time. It also works to know how some climatic factors such as the average wind speed, relative humidity, atmospheric pressure above sea level, and the maximum temperature which represent explanatory (independent) variables , respectively. The effects on the number of occurrences of dust storms represent the adopted variable (Y) in Baghdad Governorate for the period from 2008 to mid-2013. The researchers used the Generalized Partial Linear Regression Model (GPLRM) consist of fourteen models, after determining the best link function for each model. Then we compared these models to determine the best model that represents this data with the best representation using the Akaike information criterion (AIC), the Schwartz information criterion (BIC), and the determination Coefficient criterion . We also used the program (i- xplore) in the calculation, and we have concluded that the best model is the model in which the variable Â relative humidity and Â the maximum temperature in the parametric part, i.e., linear and stable. On the other hand, the variable Â average wind speed and the variable Â atmospheric pressure above sea level are non-parametric and their behavior is non-linear and unstable. The researchers consider that Baghdad Governorate suffers from this negative phenomenon as well as in general in Iraq. Besides, the effect of the variable Â relative humidity is a decreasingly negative effect, while the effect of the variable Â maximum temperature, it is an increasingly positive effect.

#### Keywords

#### Full Text:

PDF#### References

Hardle, W., Muller. M, Sperlich, S & Werwatz, A, (2004)," Nonparametric and Semi parametric Models an Introduction ", Spring Edition..

Hardle. W, Mammen E. and Muller, M., (1998),"Testing Parametric versus Semi parametric Modeling in Generalized Linear Models", Journal of the American Statistical Association, Vol.93, No.444, pp. (1461-1474).

Liu, X., (2011) , â€œ penalized variable selection for semiparametric regression models â€ , submitted in partial fulfillment of the requirements for the degree doctor of philosophy â€“ University of Rochester , New York.

Abdous .B, Kokonendj.C,Senga.T., (2012) ,"On Semi Parametric Regression for count explanatory variables", Journal of Statistical Planning and Inference, pp.(1537-1548).

Akkus, O. , (2011) , â€œ Xplore package for the popular parametric and semi-parametric single index models â€ , Journal of science ,vol.24 , No.4, pp.(753-762) .

Al-Hussaini, Maryam Abdul-Hussein, (2014), "Building and applying linear mixed regression models in the environmental field", Master Thesis in Statistics, College of Administration and Economics, University of Baghdad.

Al-sharot, Muhammad H. and Khanjar, Muhammad T., (2016) ,â€œComparing some semi-parametric estimators using simulationâ€ ,Republic of Iraq , College of Computer Science and Mathematics , Al-Qadisiyah University.

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., Yidris, N., & Fattahi, A. (2020). Experimental and numerical investigation of the mechanical behavior of full-scale wooden cross arm in the transmission towers in terms of load-deflection test. Journal of Materials Research and Technology, 9(4), 7937-7946.â€

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., & Yidris, N. (2020). Conceptual design of the cross-arm for the application in the transmission towers by using TRIZâ€“morphological chartâ€“ANP methods. Journal of Materials Research and Technology, 9(4), 9182-9188.â€

Donald W. K., Andrews, (1991), "Asymptotic normality of series estimators for Non parametric And Semi Parametric Regression Models" EconometricaVol.59, No.2, pp. (307-345).

Engle, R. F., Granger, C.W. J., Rice, J. & Weiss, A., (1986) ,"Semi parametric estimators of the relation between weather and electricity "sales, Journal of the American Statistical Association, Vol. 81, No. 394, pp.(310-320).

Eubank R.L., Whitney P., (1989),"Convergence Rates for Estimation in Certain partially linear models", journal of statistical planning and inference, Vol. 23, pp. (33-43), north Holland.

Gao.Jiti, (1995), "Asymptotic Theory for Partly linear models Communications in Statistics- Theory and Methods", 24:8.

German Aneiros & Alejandro Quintela, (2001)," Asymptotic properties in partial linear models under dependence", Socicdad de Estadlsticac Invcstigacin Operative Test, Vol. 10, No.2, pp. (333-355).

Haggag. Magda, (2007), "On the Estimation of a Semi parametric Generalized Linear Model", Interstat.atat, Journals .net articles. 0707004, pdf.

Hardle W and Huang L.S, (2013), "Analysis of Deviance in Generalized Partial Linear Models", SFB649, Humboldt-Universitat zu Berlin, Spandauer Strabe. 1, (D-10178), Berlin.

Hardle W, and Liang, H. & Gao., J., (2000),"Partilly Linear Models", Heidelberg: Physica â€“Verlag.

Muller, Marlene, (2000), "Semi parametric Extension GLMz", der Humboldt- Universitat zu Berlin.

Qasem , Habeb ,S. ,(2018), " Comparison parametric & semi-parametric single index model With application " , Master Thesis, College of Administration and Economics ,Al-Mustansiriya University, Baghdad.

Ruppert, D., Wand, M. and Carroll, R., (2003)," Semi parametric Regression", Cambridge University Press.

Saleh, Tariq A., (2016) ."Some semi-parametric methods in estimating and choosing the variable for a single index model", PhD thesis, College of Administration and Economics, University of Baghdad.

DOI: http://dx.doi.org/10.18517/ijaseit.10.5.12675

### Refbacks

- There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development