Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors

Yossy Maynaldi Pratama; Adji Achmad Rinaldo Fernandes; Ni Wayan Surya Wardhani; Rosita Hamdan

doi:10.31764/jtam.v8i2.20192

Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors

Yossy Maynaldi Pratama, Adji Achmad Rinaldo Fernandes, Ni Wayan Surya Wardhani, Rosita Hamdan

Abstract

The nonparametric approach is an appropriate approach for patterns of relationships between predictor variables and response variables that are not or have not been known in form. In other words, there is no complete information about the pattern of relationships between variables. Curve estimation is determined based on relationship patterns in existing data. The nonparametric approach has great flexibility for estimating regression curves. This study aims to form a model on investor interest factors in improving tourism investment decisions with a nonparametric approach. The nonparametric method used is the smoothing spline regression method. The smoothing spline method is used because the modeling results from the smoothing spline approach can follow the relationship model between variables contained in the data. Thus, this method really helps researchers to model relationships between variables that are not linear and whose linear form is unknown. The results of the analysis showed that the nonparametric smoothing spline regression analysis method could model data by 94.63%, indicates that data variance can be explained by 94.63% with models, while other variance outside the study explain the remaining 5.37%. That is, investment motivation is one of the most important factors to improve investment decisions.

Keywords

Nonparametric; Smoothing Spline; Tourism; Investment.

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References

Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413

Connell, J. (2013). Contemporary medical tourism: Conceptualisation, culture and commodification. Tourism Management, 34, 1–13. https://doi.org/10.1016/j.tourman.2012.05.009

Du, H., & Bentler, P. M. (2022). Distributionally weighted least squares in structural equation modeling. Psychological Methods, 27(4), 519–540. https://doi.org/10.1037/met0000388

Fernandes, A. A. R., Hutahayan, B., Solimun, Arisoesilaningsih, E., Yanti, I., Astuti, A. B., Nurjannah, & Amaliana, L. (2019). Comparison of Curve Estimation of the Smoothing Spline Nonparametric Function Path Based on PLS and PWLS in Various Levels of Heteroscedasticity. IOP Conference Series: Materials Science and Engineering, 546(5). https://doi.org/10.1088/1757-899X/546/5/052024

Fernandes, A. A. R., Nyoman Budiantara, I., Otok, B. W., & Suhartono. (2014a). Spline estimator for bi-responses nonparametric regression model for longitudinal data. Applied Mathematical Sciences, 8(113–116), 5653–5665. https://doi.org/10.12988/ams.2014.47566

Fernandes, A. A. R., Nyoman Budiantara, I., Otok, B. W., & Suhartono. (2014b). Spline estimator for bi-responses nonparametric regression model for longitudinal data. Applied Mathematical Sciences, 8(113–116), 5653–5665. https://doi.org/10.12988/ams.2014.47566

Gu, C. (2014). Journal of Statistical Software Smoothing Spline ANOVA Models: R Package gss. http://www.jstatsoft.org/

Gujarati, D. N. (2003). Basic Econometrics (L. Sutton & A. Bright, Eds.; Fourth Edition). Gary Burke.

Hainmueller, J., Mummolo, J., & Xu, Y. (2019). How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice. Political Analysis, 27(2), 163–192. https://doi.org/10.1017/pan.2018.46

Hidayat, R., Budiantara, I. N., Otok, B. W., & Ratnasari, V. (2021). The regression curve estimation by using mixed smoothing spline and kernel (MsS-K) model. Communications in Statistics - Theory and Methods, 50(17), 3942–3953. https://doi.org/10.1080/03610926.2019.1710201

Hidayati, L., Chamidah, N., & Nyoman Budiantara, I. (2019). Spline Truncated Estimator in Multiresponse Semiparametric Regression Model for Computer based National Exam in West Nusa Tenggara. IOP Conference Series: Materials Science and Engineering, 546(5), 052029. https://doi.org/10.1088/1757-899X/546/5/052029

Higgins-Desbiolles, F. (2018). Sustainable tourism: Sustaining tourism or something more? Tourism Management Perspectives, 25, 157–160. https://doi.org/10.1016/j.tmp.2017.11.017

Higgins-Desbiolles, F. (2021). The “war over tourism”: challenges to sustainable tourism in the tourism academy after COVID-19. Journal of Sustainable Tourism, 29(4), 551–569. https://doi.org/10.1080/09669582.2020.1803334

Ibacache-Pulgar, G., Villegas, C., López-Gonzales, J. L., & Moraga, M. (2023). Influence measures in nonparametric regression model with symmetric random errors. In Statistical Methods and Applications (Vol. 32, Issue 1, pp. 1–25). Institute for Ionics. https://doi.org/10.1007/s10260-022-00648-z

Kang, S., Kim, T., & Chung, W. (2020). Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks. Sensors, 20(4), 1159. https://doi.org/10.3390/s20041159

Kusumawati, Y. T. (2020). The Effect Of Financial Performance And Growth Opportunity On Investment Decisions In Food And Beverage Companies In Indonesia Stock Exchange. Jurnal Ekonomi Dan Manajemen, 14(1), 144–157. https://doi.org/10.30650/jem.v14i1.253

Lai, M. J., & Wang, L. (2013). Bivariate penalized splines for regression. Statistica Sinica, 23(3), 1399–1417. https://doi.org/10.5705/ss.2010.278

Lestari, B., Fatmawati, Budiantara, I. N., & Chamidah, N. (2018). Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators. Journal of Physics: Conference Series, 1097(1). https://doi.org/10.1088/1742-6596/1097/1/012091

Liu, A., & Wang, Y. (2004). Hypothesis testing in smoothing spline models. Journal of Statistical Computation and Simulation, 74(8), 581–597. https://doi.org/10.1080/00949650310001623416

Mardianto, M. F. F., Gunardi, & Utami, H. (2023). Interval estimation for nonparametric regression using Fourier series estimator in longitudinal data. 030010. https://doi.org/10.1063/5.0103799

Mariati, N. P. A. M., Budiantara, I. N., & Ratnasari, V. (2021). The Application of Mixed Smoothing Spline and Fourier Series Model in Nonparametric Regression. Symmetry, 13(11), 2094. https://doi.org/10.3390/sym13112094

Matteson, D. S., & James, N. A. (2014). A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data. Journal of the American Statistical Association, 109(505), 334–345. https://doi.org/10.1080/01621459.2013.849605

Mielke, J. (2015). An ultrasound study of Canadian French rhotic vowels with polar smoothing spline comparisons. The Journal of the Acoustical Society of America, 137(5), 2858–2869. https://doi.org/10.1121/1.4919346

Nurcahayani, H., Budiantara, N., & Zain, I. (2021). Mathematics The Curve Estimation of Combined Truncated Spline and Fourier Series Estimators for Multiresponse Nonparametric Regression. https://doi.org/10.3390/math

Nyoman Budiantara, I., & Lestari, B. (2019). Comparison of Smoothing and Truncated Spline Estimators in Estimating Blood Pressure Models. www.ijicc.net

Ouyang, L., Solberg, T., & Wang, J. (2011). Effects of the penalty on the penalized weighted least-squares image reconstruction for low-dose CBCT. Physics in Medicine and Biology, 56(17), 5535–5552. https://doi.org/10.1088/0031-9155/56/17/006

Pajar, R. C., & Pustikaningsih, A. (2017). Pengaruh Motivasi Investasi dan Pengetahuan Investasi terhadap Minat Investasi di Pasar Modal Pada Mahasiswa FE UNY.

Sifriyani, Haryatmi, Budiantara, I. N., & Gunardi. (2017). Geographically weighted regression with spline approach. Far East Journal of Mathematical Sciences, 101(6), 1183–1196. https://doi.org/10.17654/MS101061183

Wadhvani, R., & Shukla, S. (2019). Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve. Wind Engineering, 43(3), 225–232. https://doi.org/10.1177/0309524X18780398

Wahyuni, D., Sari, I. K. P., Prasetio, A., & Berlianaldo, M. (2023). Environmental Sustainability Analysis in the Tourism Sector: A Perspective on Resilient and Sustainable Tourism Development in Indonesia. https://www.researchgate.net/publication/372405393

Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing Parameter and Model Selection for General Smooth Models. Journal of the American Statistical Association, 111(516), 1548–1563. https://doi.org/10.1080/01621459.2016.1180986

Wylasmi, V. (2016). Regresi Nonparametrik Dengan Pendekatan Smoothing Spline Pada Data Longitudinal (Aplikasi Pada Pertumbuhan Berat Badan Bayi Di Kota Malang).

Xiang, D., & Wahba, G. (1995). Testing the Generalized Linear Model Null Hypothesis versus Smooth" Alternatives.

Xu, B., Luo, Y., Xu, R., & Chen, J. (2021). Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model. Energy, 236. https://doi.org/10.1016/j.energy.2021.121452

Xu, P., Liu, J., & Shi, Y. (2023). Almost unbiased weighted least squares location estimation. Journal of Geodesy, 97(7), 68. https://doi.org/10.1007/s00190-023-01742-0

DOI: https://doi.org/10.31764/jtam.v8i2.20192