Premarital Sex Behavior Model with Lasso Generalized Linear Mixed Model and Group Lasso Generalized Linear Mixed Model
DOI:
https://doi.org/10.29313/statistika.v23i1.1953Keywords:
GLMM LASSO, GLMM Group LASSO, Premarital SexAbstract
ABSTRACT
Premarital sexual behavior is sexual behavior that is carried out between men and women without legal marriage. As the number of premarital sex increases, efforts need to take. One that can do is to identify the main factors contributing to reducing or increasing premarital sex behavior by a Regression model. In the context of sexual behavior, environmental influences cannot be ignored. GLMM is used to model data that is grouped into certain Groups, include environment effect that is modeled as mixed effect in GLMM. In terms of parsimony, the LASSO method can do selection variables. This research uses GLMM LASSO and GLMM Group LASSO as a model to approach the data. The best model that describes premarital sex behavior in South Sulawesi is the GLMM Group LASSO model based on the greatest AUC value. The variables that significantly influence the model are Type of Residence (X_1), Education Level (X_2), Literacy (X_3), Internet use (X_4), Knowledge of Contraceptive Methods (X_6), Health Insurance Ownership (X_7), Employment Status (X_8), Knowledge of Sexually Transmitted Diseases (X_9). By knowing the factors that influence premarital sex behavior, the government is expected to take the appropriate action for handling it.
References
Agresti A. 2013. Categorical Data Analysis, Third Edition. Canada (CA): John Wiley and Sons.
Bach, F. R. (2008). Consistency of the group Lasso and multiple kernel learning. J. Mac. Learn. Res., to appear.
Bakin, S. (1999) Adaptive regression and model selection in data mining problems. Ph.D. Thesis. Australian National University, Canberra.
Balitbang Kemenkes RI. 2013. Riset Kesehatan Dasar; RISKESDAS. Jakarta: Balitbang Kemenkes RI.
Breiman, L. 1995. Better subset regression using the nonnegative garrote. Technometrics, Vol. 37, No 4, pp. 373-383.
Breslow, N.E dan Clayton, D.G. 1993. Approximate inference in generalized linear mixed models. J. of the American Statistical Association 88, 9-25.
Breslow, N. E., & Lin, X. (1995). Bias Correction in Generalised Linear Mixed Models with a Single Component of Dispersion. Biometrika, 82(1), 81–91. https://doi.org/10.2307/2337629
E.Gbar, E., W.Stroup, W., S.McCarter, K., Durham, S., J.Young, L., Christman, M., Kramer, M. (2012). Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences. USA: Book and Multimedia Publishing Committee.
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least Angle Regression. The Annals of Statistics, 32(2), 407–451. http://www.jstor.org/stable/3448465
Erlinda. 2014. Upaya Peningkatan Anak dari Bahaya Kekerasan, Pelecehan dan Eksploitasi. Jakarta: KPAI.
Gad A M, Kholy R S, 2012, Generalized Linear Mixed Models for Longitudinal Data, International Journal of Probability and Statistics 2012, 1(3): 67-73 DOI:10.5923/j.ijps. 20120103.03, Cairo.
Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biom J. 2010 Feb;52(1):70-84. doi: 10.1002/bimj.200900028. PMID: 19937997.
Gorunescu F. 2011. Data Mining: Concepts, Models, and Techniques. Berlin: Springer
Guiella, G., & Madise, N. J. (2007). HIV/AIDS and Sexual-Risk Behaviors among Adolescents: Factors Influencing the Use of Condoms in Burkina Faso. African Journal of Reproductive Health / La Revue Africaine de La Santé Reproductive, 11(3), 182–196. https://doi.org/10.2307/25549739
Groll A, Tutz G. 2012. Variable selection for generalized linear mixed models by L1-penalized estimation. Statistics and Computing. 24: 137-154. DOI:10.1007/s11222-012-9359-z
Huang J, Zhang T. 2009. The Benefit of Group Sparsity. Ann Statist.38:5277-5286.
James G, Witted D, Hastie T, Tibshirani R. 2021. An Introduction to Statistical Learning with Application in R, Second Edition. New York (US): Springer.
Kachman, S. D. 1998. An Introduction to generalized linear mixed models. Dept. of Biometry, Univ. of Nebraska-Lincoln.
Kurnia, A. 2000. Pendekatan GEE dan Quasi-Likelihood dalam Generalized Linear Mixed Models. Bogor: Institut Pertanian Bogor
Lounici, K., Pontil,M., Van, GS., and Tsybakov, AB. 2011. Oracle Inequalities and Optimal Inference Under Group Sparsity. Ann Statist 39:2164-2204.
Muslim A, Hayati M, Sartono B, Notodiputro KA. 2018. A Combined Modeling of Generalized Mixed Model and LASSO Technique for Analyzing Monthly Rainfall Data, IOP Conference Series: Earth and Environmental Science.
Nardi, Y., and Rinaldo, A. 2008. One of the Asymptotic Properties of The Group LASSO Estimator for Linear Models. Electronic Journal of Statistics. ISSN: 1935-7524.
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1): 267–288.
Utomo ID, McDonald P. Adolescent reproductive health in Indonesia: contested values and policy inaction. Stud Fam Plann. 2009 Jun;40(2):133-46. doi: 10.1111/j.1728-4465.2009.00196.x. PMID: 19662805.
Yuan M, Lin Y. 2006. Model Selection and Estimation in Regression with Grouped Variables. Journal of the Royal Statistical Society Series B 68(1): 49-67
YUNI RAHYANI, NI KOMANG and UTARINI, ADI and AGUS WILOPO, SISWANTO and HAKIMI, MOHAMMAD (2012) Perilaku Seks Pranikah Remaja. Jurnal Kesehatan Masyarakat Nasional, Kesmas, 7 (4). pp. 180-185.
Wolfinger, R.D., & O'connell, M.A. (1993). Generalized linear mixed models a pseudo-likelihood approach. Journal of Statistical Computation and Simulation, 48, 233-243.