Empirical models for tool forces prediction of drag-typed picks based on principal component regression and ridge regression methods

2017 ◽  
Vol 62 ◽  
pp. 75-95 ◽  
Author(s):  
Xiang Wang ◽  
Yunpei Liang ◽  
Qingfeng Wang ◽  
Zhenyu Zhang
Keyword(s):  
Ridge Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Empirical Models ◽  
Regression Methods

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

scholarly journals Constructed Analogs and Linear Regression

2013 ◽  
Vol 141 (7) ◽  
pp. 2519-2525 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole
Keyword(s):  
Linear Regression ◽  
Linear Combination ◽  
Ridge Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Local Linear Regression ◽  
Analog Method ◽  
Local Linear ◽  
Nonlinear Extensions

Abstract The constructed analog procedure produces a statistical forecast that is a linear combination of past predictand values. The weights used to form the linear combination depend on the current predictor value and are chosen so that the linear combination of past predictor values approximates the current predictor value. The properties of the constructed analog method have previously been described as being distinct from those of linear regression. However, here the authors show that standard implementations of the constructed analog method give forecasts that are identical to linear regression forecasts. A consequence of this equivalence is that constructed analog forecasts based on many predictors tend to suffer from overfitting just as in linear regression. Differences between linear regression and constructed analog forecasts only result from implementation choices, especially ones related to the preparation and truncation of data. Two particular constructed analog implementations are shown to correspond to principal component regression and ridge regression. The equality of linear regression and constructed analog forecasts is illustrated in a Niño-3.4 prediction example, which also shows that increasing the number of predictors results in low-skill, high-variance forecasts, even at long leads, behavior typical of overfitting. Alternative definitions of the analog weights lead naturally to nonlinear extensions of linear regression such as local linear regression.

scholarly journals Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance

Horticulturae ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 56
Author(s):  
Milon Chowdhury ◽  
Viet-Duc Ngo ◽  
Md Nafiul Islam ◽  
Mohammod Ali ◽  
Sumaiya Islam ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Regression Methods ◽  
Freeze Dried ◽  
Plant Factory ◽  
Functional Components

The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.

scholarly journals Penggunaan Metode Stepwise pada Pemodelan Perencanaan Track Quality Index (TQI) untuk Kereta Api Semicepat Indonesia

2019 ◽  
Vol 4 (1) ◽  
pp. 114
Author(s):  
Alfisyahrina Hapsery ◽  
Reysha Rizki Amanda Lubis
Keyword(s):  
Ridge Regression ◽  
Quality Index ◽  
Principal Component Regression ◽  
Principal Component ◽  
Track Quality

Dalam analisis regresi, salah satu asumsi yang harus dipenuhi adalah tidak adanya hubungan antar variabel independen. Hubungan yang kuat antar variabel independen disebut dengan multikolinieritas. Berbagai metode dapat menanggulangi kasus multikolinieritas, semua itu bergantung pada tujuan dari penelitian. Beberapa metode tersebut adalah ridge regression, principal component regression, regresi robust dan pemilihan model terbaik. Pada penelitian ini, metode pemilihan model terbaik dipilih untuk digunakan karena bertujuan untuk menentukan variabel independen yang signifikan dengan mempertimbangkan korelasi parsial pada data track quality index (TQI) kereta api Indonesia. Untuk mengukur besarnya TQI diperlukan empat indikator yang kemudian menjadi variabel dalam penelitian ini, yaitu lebar jalur, angkatan, listringan dan pertinggian. Hasil analisis menunjukkan variabel pertinggian, angkatan dan listringan berpengaruh besarnya nilai TQI dengan variasi data yang dapat dijelaskan model sebesar 99,7%.

Prediction of the voluntary intake of grass silages by beef cattle. 2. Principal component and ridge regression analyses

1990 ◽  
Vol 50 (3) ◽  
pp. 439-454 ◽  
Author(s):  
A. J. Rook ◽  
M. S. Dhanoa ◽  
M. Gill
Keyword(s):  
Fatty Acids ◽  
Ridge Regression ◽  
Volatile Fatty Acids ◽  
A Priori ◽  
Principal Component Regression ◽  
Live Weight ◽  
Principal Component ◽  
Ammonia Nitrogen ◽  
Linear Functions ◽  
Factors Affecting

ABSTRACTData on individually recorded silage intake (SDMI), concentrate intake and live weight of steers and data on silage composition including toluene dry matter, pH, total nitrogen, ammonia nitrogen, volatile fatty acids, digestibility and fibre measures obtained from experiments at three sites were used.Correlation and principal component analyses indicated that there was severe collinearity among a number of the variables, particularly among various fermentation characteristics, between different measures of digestibility and between different measures of fibre. This collinearity was shown to have caused instability in least-squares multiple regression coefficients of silage intake on other variables previously obtained from the same data (Rook and Gill, 1990). Principal component regression and ridge regression allowed the derivation of new coefficients which were more stable and more in line with a priori expectations from experimental results. Linear functions of the original variables and the use of models containing fewer variables also proved effective in overcoming the problems of collinearity.Fermentation characteristics were shown to be the most important silage factors affecting intake and butyric acid was shown to be more important than other volatile fatty acids or ammonia nitrogen. Neutral-detergent fibre was found to be better related to intake than other fibre or digestibility measures.

scholarly journals Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis

2019 ◽  
Vol 7 (8) ◽  
pp. 1166 ◽  
Author(s):  
Soner Çankaya ◽  
Samet Eker ◽  
Samet Hasan Abacı
Keyword(s):  
Least Squares ◽  
Ridge Regression ◽  
Least Squares Method ◽  
Principal Component Regression ◽  
Live Weight ◽  
Principal Component ◽  
Multiple Regression Model ◽  
Estimation Methods ◽  
Body Weights ◽  
Research Findings

The aim of this study was to compare estimation methods: least squares method (LS), ridge regression (RR), Principal component regression (PCR) to estimate the parameters of multiple regression model in situations when the underlying assumptions of least squares estimation are untenable because of multicollinearity. For this aim, the effect of some body measurements on body weights (height at withers and rumps, body length, chest width, chest girth and chest depth, front, middle and hind rump width) obtained from totally 85 Karayaka lambs at weaning period raised at Research Farm of Ondokuz Mayis University was examined. Mean square error, R2 value and significance of parameters were used to evaluate estimator performance. The multicollinearity, between front and middle rump width which were used to estimate live weight, was eliminated by using RR and PCR. Although research findings showed that RR method had the smallest MSE and the highest R2 value, the estimates of PCR were determined to be more consistent when the importance tests of parameters were taken into account. The results showed that principal component regression approach should be used to estimate the live weight of Karayaka lambs at weaning period.

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