DOI: https://doi.org/10.14710/medstat.13.2.125-135

IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province)

*Alfi Fadliana  -  Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Raden Rahmat, Indonesia
Henny Pramoedyo  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Rahma Fitriani  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
Fulltext View|Download
Keywords: Stunting; Multicollinearity; Geographically Weighted Regression; Locally Compensated Ridge

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
Front Matter Vol. 13 No. 2 2020 IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province) ANALYSIS OF SRONDOL-JATINGALEH TOLL QUEUE SYSTEM AT SEMARANG CITY IN THE END OF YEAR 2018 WITH AUTOMATIC TOLL GATE SYSTEM USING LOGISTIC DISTRIBUTION APPROACH A SIMULATION STUDY OF FIXED-B ASYMPTOTIC DISTRIBUTIONS IN LINEAR PANEL MODELS WITH FIXED EFFECTS More recent articles
Most cited articles
MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda PEMODELAN INFLASI BERDASARKAN HARGA-HARGA PANGAN MENGGUNAKAN SPLINE MULTIVARIABEL DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS More cited articles
  1. Central Bureau of Statistics. (2018a). District/City Poverty Data and Information in 2017. Jakarta: Central Bureau of Statistics
  2. Central Bureau of Statistics. (2018b). Human Development Index in 2017. Jakarta: Central Bureau of Statistics
  3. Czarnota, J., Wheeler, D. C., & Gennings, C. (2015). Evaluating Geographically Weighted Regression Models for Environmental Chemical Risk Analysis. Cancer Informatics, 14, 117–127
  4. East Nusa Tenggara Province Central Bureau of Statistics. (2017). East Nusa Tenggara Province People’s Welfare Statistics in 2017. Kupang: East Nusa Tenggara Province Central Bureau of Statistics
  5. Fadliana, A., Pramoedyo, H., & Fitriani, R. (2019). Parameter Estimation of Locally Compensated Ridge-Geographically Weighted Regression Model. IOP Conference Series: Materials Science and Engineering, 546(5)
  6. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: the Analysis of Spatially Varying Relationships. Chichester, West Sussex: John Wiley & Sons Ltd
  7. Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel : an R Package for Exploring Spatial Heterogeneity. Journal of Statistical Software, 63(17), 1–50
  8. Indonesian Ministry of Health. (2013). Baseline Health Research in 2013. Jakarta: Indonesian Ministry of Health
  9. Indonesian Ministry of Health. (2016). Situtation of Stunted Under-Fives. Jakarta: Indonesian Ministry of Health
  10. Indonesian Ministry of Health. (2017). Results of Nutritional Status Survey and Its Explanation in 2016. Jakarta: Indonesian Ministry of Health
  11. Indonesian Ministry of Health. (2018). Pocket Book of Nutritional Status Survey in 2017. Jakarta: Indonesian Ministry of Health
  12. Páez, A., Farber, S., & Wheeler, D. (2011). A Simulation-Based Study of Geographically Weighted Regression as A Method for Investigating Spatially Varying Relationships. Environment and Planning A, 43(12), 2992–3010
  13. Tu, Y. K., Gunnell, D., & Gilthorpe, M. S. (2008). Simpson’s Paradox, Lord’s Paradox, and Suppression Effects are the Same Phenomenon - The Reversal Paradox. Emerging Themes in Epidemiology, 5, 1–9
  14. Wheeler, D. C. (2007). Diagnostic Tools and A Remedial Method for Collinearity in Geographically Weighted Regression. Environment and Planning A, 39(10), 2464–2481
  15. Wheeler, D. C. (2009). Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso. Environment and Planning A, 41(3), 722–742
  16. Wheeler, D. C., & Calder, C. A. (2007). An Assessment of Coefficient Accuracy in Linear Regression Models with Spatially Varying Coefficients. Journal of Geographical Systems, 9(2), 145–166
  17. Wheeler, D. C., & Tiefelsdorf, M. (2005). Multicollinearity and Correlation among Local Regression Coefficients in Geographically Weighted Regression. Journal of Geographical Systems, 7(2), 161–187
  18. World Health Organization (WHO). (2010). Nutrition Landacape Information System (NLIS) Country Profile Indicators: Interpretation Guide. Geneva: WHO Press

Last update:

  1. SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

    Dewi Retno Sari Saputro, Sulistyaningsih Sulistyaningsih, Purnami Widyaningsih. MEDIA STATISTIKA, 14 (1), 2021. doi: 10.14710/medstat.14.1.89-97

Last update: 2024-04-26 13:13:14

No citation recorded.