سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,504
تعداد مشاهده مقاله124,122,766
تعداد دریافت فایل اصل مقاله97,230,972
Islamiyati, Anna. (1399). Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model. , 31(2), 175-183. doi: 10.22059/jsciences.2020.286949.1007435
Anna Islamiyati. "Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model". , 31, 2, 1399, 175-183. doi: 10.22059/jsciences.2020.286949.1007435
Islamiyati, Anna. (1399). 'Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model', , 31(2), pp. 175-183. doi: 10.22059/jsciences.2020.286949.1007435
Islamiyati, Anna. Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model. , 1399; 31(2): 175-183. doi: 10.22059/jsciences.2020.286949.1007435

Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model

Journal of Sciences, Islamic Republic of Iran
مقاله 9، دوره 31، شماره 2، تیر 2020، صفحه 175-183    اصل مقاله (338.69 K)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2020.286949.1007435
نویسنده
Anna Islamiyati*
Department of Statistics Faculty of Mathematics and Natural Sciences Hasanuddin University, Makassar, Sulawesi Selatan, Indonesia.
چکیده
Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The regression function with two predictors in the non-parametric model will have two different non-parametric regression functions. Therefore, we propose the use of two smoothing parameters in the bi-variate predictor non-parametric regression model. We demonstrated its ability through longitudinal data simulation studies with a comparison of one smoothing parameter. It was done on several numbers of subjects with repeated measurements. The generalized cross validation value which is a measure of the model's ability is poured through the box plot. The results show that the use of two smoothing parameters is more optimal than one smoothing parameter. It was seen through a smaller generalized cross validation value on the use of two smoothing parameters. Application of blood sugar level data for patients with two smoothing parameters produced a penalized spline bi-variate predictor regression model with several segments of change patterns. There are five patterns at the time of treatment and blood pressure with the number of smoothing parameters is two, namely 0.39 and 0.73.
کلیدواژه‌ها
Bi-variate؛ Longitudinal Data؛ Penalized Spline؛ Smoothing Parameter
مراجع
  1. Ruppert D., and Carrol R. J. Spatially-adaptive penalties for spline fitting. Aust. N. Z. J. Stat.., 42(2) : 205-223 (2000).
  2. Claeskens G., Kribovokova T., and Opsomer J. D. Asymptotic properties of penalized spline estimators. Biometrika, 96(3) : 529-544 (2009).
  3. Montoya L. E., Ulloa N., and Miller V. A simulation study comparing knot selection methods with equally spaced knot in a penalized regression spline. Int. J. Stat. Probab., 3(3): 96-110 (2014). 
  4. Lee T. C. M., and Oh H. S. Robust penalized regression spline fitting with application to additively mixed modeling. Comput. Stat., 22: 159-171 (2007). 
  5. Wang  B., Shi W., and Miao Z. Comparative analysis for robust penalized spline smoothing methods. Math. Probl. Eng., 2014(Article ID 642475) : 339-353 (2014). 
  6. Islamiyati A., Fatmawati, and Chamidah N. Estimation of covariance matrix on bi-response longitudinal data analysis with penalized spline regression. J. Phys.: Conf. Ser., 979: 012093 (2018). 
  7. Islamiyati A., Fatmawati, and Chamidah, N. Changes in blood glucose 2 hours after meals in type 2 diabetes patients based on length of treatment at Hasanuddin University Hospital, Indonesia.  Rawal Medical J., 45(1): 31-34 (2020).
  8. Zaherzadeh A., Rasekh A., and Babadi B. Diagnostic measures in ridge regression model with AR(1) errors under the stochastic linear restrictions. J. Sci. I. R. Iran, 29: 67-78 (2018).
  9. Mohammadi H., and Rasekh A. R. Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors. J. Sci. I. R. Iran, 30(3): 271-285 (2019).
  10. Heckman N. E., and Ramsay J. O. Penalized regression with model-based penalties. Can. J. Stat., 28: 241-258 (2000).
  11. Yao, and Lee. Penalized spline models for functional principal component analysis. J. R. Stat. Soc. B., 68(1) : 3-25 (2006).
  12. Aydin D., and Yilmaz E. Modified spline regression based on randomly right-censored data: A comparative study. Commun. Stat-Simul. C., 47(9): 2587-2611 (2018).
  13. Chamidah N., and Lestari B. Spline estimator in homoscedastic multi-response nonparametric regression model in case of unbalanced number of observations. Far East J. Math. Sci.,100(9) : 1433-1453 (2016).
  14. Chamidah N., and Rifada M. Local linear estimation in bi-response semiparametric regression model for estimating median growth charts of children. Far East J. Math. Sci.,99(8): 1233-1244 (2016).
  15. Lestari B., Fatmawati, and Budiantara I. N. Estimation of regression function in multi-response nonparametric regression model using smoothing spline and kernel estimators. J. Phys.: Conf. Ser.,1097:012091 (2018).
  16. Chamidah N., Gusti K. H., Tjahjono E., and Lestari B. Improving of classification accuracy of cyst and tumor using local polynomial estimator. Telkomnika,17: 1492-1500 (2019).
  17. Lin X., and Zhang D. Inference in generalized additive mixed models by using smoothing splines. J. R. Stat. Soc. B., 61: 381-400 (1999).
  18. Ni X., Zhang D., and Zhang H. H. Variable selection for semiparametric mixed models in longitudinal studies. Biometrics, 66 : 79-88 (2010).
  19. Lai M. J., and Wang L. Bivariate penalized spline for regression. Stat. Sin., 23: 1399-1417 (2013).
 

آمار
تعداد مشاهده مقاله: 679
تعداد دریافت فایل اصل مقاله: 556


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب