Robust Regression on Simple Housing Environmental Performance Measurement

Authors

  • Hafizah Miranda Putri Hrp Universitas Sumatera Utara
  • Esther Sorta Mauli Nababan Universitas Sumatera Utara
  • Tulus Universitas Sumatera Utara

DOI:

10.33395/sinkron.v8i2.13725

Abstract

Robust regression in residential environmental performance measurement can provide significant benefits. This method can help identify and overcome uncertainties in measurement results so that decision making related to environmental protection and management can be done more accurately. This type of research is quantitative with a descriptive approach.  The results of this study indicate that the results of the M-estimate robust regression estimation on simple housing environmental performance obtained the M-estimate robust regression equation, namely ŷ = 15.562+0.476X1-0.453X2+0.222X3-0.427X4. Based on the p-value test results, it shows that population attributes, house attributes, energy consumption volume and utilities and services have a significant effect on environmental performance. The results of the hypothesis testing of the M-estimation robust regression method on the environmental performance of simple housing obtained that the variables of population attributes and energy consumption volume have a positive and significant effect on environmental performance while the variables of house attributes and utilities and services have a negative and significant effect on environmental performance.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Asiaei, K., & Jusoh, R. (2017). Using a robust performance measurement system to illuminate intellectual capital. International Journal of Accounting Information Systems, 26, 1–19. https://doi.org/https://doi.org/10.1016/j.accinf.2017.06.003

Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of robust optimization. SIAM Review, 53(3), 464–501.

Cabana, E., Lillo, R. E., & Laniado, H. (2020). Robust regression based on shrinkage with application to Living Environment Deprivation. Stochastic Environmental Research and Risk Assessment, 34(2), 293–310. https://doi.org/10.1007/s00477-020-01774-4

Dubois, D., & Prade, H. (2015). Possibility Theory and Its Applications: Where Do We Stand? In Springer Handbook of Computational Intelligence (pp. 31–60). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_3

García, J., & Peña, A. (2018). Robust Optimization: Concepts and Applications. In Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization. InTech. https://doi.org/10.5772/intechopen.75381

Healy, K. (2005). Book Review: An R and S-PLUS Companion to Applied Regression. Sociological Methods & Research, 34(1), 137–140. https://doi.org/10.1177/0049124105277200

Huber, P. J. (1973). Robust Regression: Asymptotics, Conjectures and Monte Carlo. The Annals of Statistics, 1(5), 799–821. http://www.jstor.org/stable/2958283

Huber, P. J. (1981). Robust Statistics. John Wiley & Sons, Inc.

Malmqvist, T., & Glaumann, M. (2006). Selecting problem-related environmental indicators for housing management. Building Research & Information, 34(4), 321–333. https://doi.org/10.1080/09613210600733658

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to Linear Regression Analysis (Sixth). Wiley.

Nababan, E., Siahaan, N., Bangun, P., & Rosmaini, E. (2018). Environmental Performance Measurement of the Simple Urban Housing in Martubung Medan. IOP Conference Series: Materials Science and Engineering, 288, 012059. https://doi.org/10.1088/1757-899X/288/1/012059

Shameem, M., Kumar, C., Chandra, B., & Khan, A. A. (2017). Systematic Review of Success Factors for Scaling Agile Methods in Global Software Development Environment: A Client-Vendor Perspective. 2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW), 17–24. https://doi.org/10.1109/APSECW.2017.22

Smith, C., Guennewig, B., & Muller, S. (2022). Robust subtractive stability measures for fast and exhaustive feature importance ranking and selection in generalised linear models. Australian & New Zealand Journal of Statistics, 64(3), 339–355. https://doi.org/10.1111/anzs.12375

Downloads


Crossmark Updates

How to Cite

Hrp, H. M. P., Nababan, E. S. M. ., & Tulus. (2024). Robust Regression on Simple Housing Environmental Performance Measurement. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1226-1232. https://doi.org/10.33395/sinkron.v8i2.13725