Development of Path Analysis Based on Nonparametric Regression on Vegetative Growth and Porang’s Production Modeling

Adji Achmad Rinaldo Fernandes, Solimun, Muflikhah, Aisyah Aryandani, Abela Chairunissa, Endang Krisnawati, Alifya Al Rohimi, Fat Laili Nur Rasyidah

Abstract

This study aims to obtain the properties of the spline estimator in the Nonparametric Regression-based Path Analysis using the PWLS approach. The authors test the hypothesis on each relationship between variables in the Nonparametric Regression-based Path Analysis using the PWLS approach and obtain the optimal confidence interval for each connection between variables in Path Analysis based on Nonparametric Regression using the PWLS approach. Porang growth factors and environmental factors are still analyzed partially. This study used a multivariate approach to investigate factors that influence the growth of porang tuber production. Overall, the study was compiled using an observational research design to test the growth and production model design for porang. Furthermore, a random sampling method was carried out to observe paired quantitative data for porang growth factors, tuber size, environmental factors (vegetation, climate, and soil) in four agroforestry lands in East Java (Madiun, Nganjuk, Malang, and Bojonegoro). The research was conducted using the literature search method to obtain research data on the growth of porang tubers. The findings obtained were in the form of a review of the properties of the spline estimator in the Nonparametric Regression-based Path Analysis using the Penalized Weighted Least Square (PWLS) approach, hypothesis testing on each relationship between variables in the Nonparametric Regression-based Path Analysis using the PWLS approach, as well as some findings regarding the optimal confidence interval on each relationship between variables in the Nonparametric Regression-based Path Analysis using the PWLS approach. On the other hand, it is obtained theoretical testing of optimal confidence intervals for each relationship between variables in the Nonparametric Regression-based Path Analysis using the PWLS approach. The originality of this paper is that the author develops a non-parametric regression-based path analysis that is robust with linearity assumptions, which are applied to the modeling of negative growth and porang production.

 

 

Keywords: Path Analysis, Spline, Nonparametric, Regression, Vegetative Growth, PWLS

 

 

 


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AFRIYOL K. Tinjauan Aspek Agroklimatologi tanaman iles-iles A. onchophyllus. (Overview of the agro-climatological aspects of A. onchophyllus plant iles-iles) Thesis. Bogor, IPB University, Department of Geophysics and Meteorology, 1993.

ANTONIADIS A., GREGOIRE G., and MCKEAGUE I.W. Wavelet methods for curve Estimation. Journal of American Statistical Association, 1994, 89(2): 1340-1353.

ARISOESILANINGSIH E., INDRIYANI S., RETNOWATI R. et al. Pemodelan pertumbuhan vegetatif dan produksi umbi porang pada beberapa umur tanaman, kondisi vegetasi, tanah dan iklim agroforestry (Vegetative growth modeling and porang tuber production at several plant ages, vegetation conditions, soil and agroforestry climates). Research Report. Malang: Universitas Brawijaya, 2009.

BUDIANTARA I. N., LESTARI B., and ISLAMIYATI A. Estimator Spline Terbobot Spline Parsial Terbobot dalam Regresi Nonparametrik dan Semi parametrik Heteroskedastik Untuk Data Longitudinal (Weighted partial spline weighted spline estimators in heteroscedastic non-parametric and semi-parametric regression for longitudinal data). Final Report of the Year Competency Grants Program 1. Institute for Research and Community Service. Sepuluh Nopember Institute of Technology. Surabaya, 2009.

BUDIANTARA I.N., SUBANAR K., and SOEJOETI Z. Weighted spline estimator. Bulletin of the International Statistical Institute, 1997, 51(1): 333-334.

CRAVEN P. and WAHBA G. Smoothing noisy data with spline function: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerical Mathematics, 1979, 31(1): 377-403.

DILLON W. R. and GOLDSTEIN M. Multivariate Analysis Methods and Applications. New York: John Wiley and Sons, 1984.

EUBANK R. L. Nonparametric Regression and Spline Smoothing. 2nd ed. New York: Marcel Dekker, 1999.

FAN J. and GIJBELS I. Local Polynomial Modelling and Its Applications. New York: Chapman and Hall, 1997.

HARDLE W. Applied Nonparametric Regression. New York: Cambridge University Press, 1990.

JANSEN P.C.M., VAN DER WILK C., and HETTERSCHEID W.L.A. Amorphophallus Blume ex Decaisne. In: FLACH M. dan F. RUMAWAS (eds.) Plant resources of South-East Asia 9: Plants yielding non-seed carbohydrates. Bogor: Prosea Foundation, 1996.

ROSMAN R. and RUSLI S. Tanaman Iles-iles (Iles-iles plant)ю LITTRO special edition vol. VII No.2. Bogor: Indonesian Spice and Medicinal Plants Research Institute (BALITTRO), 1991.

SOLIMUN F. Analisis Multivariat Pemodelan Struktural (Multivariate Analysis of Structural Modeling). Malang: Citra, 2010.

SUFIANI S. Iles-iles (Amorphophallus) jenis, syarat tumbuh, budidaya dan standar mutu ekspornya (Iles-iles (Amorphophallus) species, growing conditions, cultivation and export quality standards) Bogor: Indonesian Spice and Medicinal Plants Research Institute (BALITTRO), 1993.

SUMARWOTO S. Iles-iles (Amorphophallus muelleri Blume): Deskripsi dan sifat-sifat lainnya (Iles-iles (Amorphophallus muelleri Blume): Description and other properties) Biodiversitas, 2005, 6 (3):185-190.

SUPRANTO J. Statistik: Teori dan Aplikasi (Statistics: Theory and Applications) 8th ed. Jakarta: Erlangga, 2010.

WAHBA G. Spline Models for Observational Data. Pennsylvania: SIAM, 1990.

PAN H. & KUSUNOKI K. A wavelet transform-based capacity curve estimation approach using seismic response data. Structural Control and Health Monitoring, 2018, 25(12): e2267.

VON SACHS R. Nonparametric wavelet methods for nonstationary time series. In: Probability Theory and Mathematical Statistics. Berlin: De Gruyter, 2020: 627-652.

WANG X., XU J., & ZHAO Y. Wavelet-based denoising for the estimation of the state of charge for lithium-ion batteries. Energies, 2018, 11(5): 1144.

ISLAMIYATI A. & CHAMIDAH N. Estimation of covariance matrix on bi-response longitudinal data analysis with penalized spline regression. Journal of Physics: Conference Series 2018, 979(1):012093.

OVRÉN H. & FORSSÉN P. E. Spline error weighting for robust visual-inertial fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2018), Salt Lake City, Utah, June 12-14, 2018. Washington, IEEE Computer Society, 2018: 321-329.

HETTERSCHEID W. and ITTENBACH S. Everything you always wanted to know about Amorphophallus, but were afraid to stick your nose into!!!!! Aroideana, 1996, 19: 7-13.

SANTOSA E., SUGIYAMA N., KURNIAWATI A., et al. Variation in floral morphology of Agamosporous (Amorphophallus muelleri Blume) in natural and gibberellin induced flowering. Journal of Applied Horticulture, 2018, 20(1):15-29.

SUK H. W., WEST S. G., FINE K. L., & GRIMM K. J. Nonlinear growth curve modeling using penalized spline models: A gentle introduction. Psychological Methods, 2019, 24(3): 269.

CHEN X., HEITJA, D. F., GREI, G et al. Estimating the optimal timing of surgery from observational data. Biometrics. Pub. Date: 2020-06-07. DOI: 10.1111/biom.13311.

PERPEROGLOU A., SAUERBREI W., ABRAHAMOWICZ M. et al. A review of spline function procedures in R. BMC medical research methodology, 2019, 19(1): 1-16.


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