The Design of Weather Index Insurance Using Principal Component Regression and Partial Least Squares Regression: The Case of Forage Crops

2020 ◽  
Vol 24 (3) ◽  
pp. 355-369
Author(s):  
Milton Boyd ◽  
Brock Porth ◽  
Lysa Porth ◽  
Ken Seng Tan ◽  
Shuo Wang ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Index Insurance ◽  
Least Squares Regression ◽  
Forage Crops ◽  
Weather Index ◽  
Weather Index Insurance

scholarly journals PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI KUADRAT TERKECIL PARSIAL PADA INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR

2019 ◽  
Vol 8 (4) ◽  
pp. 496-505
Author(s):  
Vetranella .T.R.A. Sinaga ◽  
Diah Safitri ◽  
Rita Rahmawati
Keyword(s):  
Least Squares ◽  
Human Development ◽  
Partial Least Squares ◽  
Human Development Index ◽  
Partial Least Squares Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Development Index ◽  
Independent Variables

The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new   independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s  is 95,85% is greater than PCR’s  = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS

Use of the Partial Least Squares Method with Acoustic Vibration Spectra as a New Grading Technique for Structural Timber

Holzforschung ◽  
2003 ◽  
Vol 57 (6) ◽  
pp. 644-652 ◽  
Author(s):  
L. Brancheriau ◽  
H. Baillères
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Least Squares Method ◽  
Principal Component Regression ◽  
Principal Component ◽  
Acoustic Vibration ◽  
Modulus Of Rupture ◽  
Least Squares Regression

Summary This study develops a high performance grading process based on the analysis of acoustic vibrations in the audible frequency range. The unique feature of the method is that the spectrum is directly applied to obtain predictive variables for estimating the modulus of elasticity and modulus of rupture. A partial least squares regression was used. This powerful method represents a compromise between principal component regression and multi-linear regression. Partial least squares regression screens for factors which account for the variance in the predictor variables and achieves the best correlation between factors and predicted variable. The method is based on projections, similar to principle components regression, whereby a set of correlated variables is compressed into a smaller set of uncorrelated factors.

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