Eigenspectra, a robust regression method for multiplexed Raman spectra analysis

2010 ◽  
Author(s):  
Shuo Li ◽  
Jean Gao ◽  
James O Nyagilo ◽  
Digant P Dave
Keyword(s):  
Raman Spectra ◽  
Robust Regression ◽  
Regression Method ◽  
Spectra Analysis

Eigenspectra, a robust regression method for multiplexed Raman spectra analysis

2013 ◽  
Vol 7 (4) ◽  
pp. 358 ◽  
Author(s):  
Shuo Li ◽  
James O. Nyagilo ◽  
Digant P. Dave ◽  
Baoju Zhang ◽  
Jean Gao
Keyword(s):  
Raman Spectra ◽  
Robust Regression ◽  
Regression Method ◽  
Spectra Analysis

scholarly journals PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 24-34
Author(s):  
Eka Destiyani ◽  
Rita Rahmawati ◽  
Suparti Suparti
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Predictor Variables ◽  
Healthy Life ◽  
Significance Level ◽  
Central Java ◽  
Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM  Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords:  Ordinary  Least  Squares  (OLS),  Multicollinearity,  Outliers,  RidgeRegression, Robust Regression, AKB.

scholarly journals C60 Filling Rate in Carbon Peapods: A Nonresonant Raman Spectra Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Fatima Fergani ◽  
Sidi Abdelmajid Ait Abdelkader ◽  
Hassane Chadli ◽  
Brahim Fakrach ◽  
Abdelhai Rahmani ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Experimental Method ◽  
Raman Spectra ◽  
Double Helix ◽  
Single Walled Carbon Nanotubes ◽  
Filling Rate ◽  
Spectra Analysis ◽  
Single Walled Carbon ◽  
Bond Polarizability ◽  
Walled Carbon Nanotubes

We calculated the nonresonant Raman spectra of C60 peapods to determine the concentration of C60 fullerenes inside single-walled carbon nanotubes. We focus on peapods with large diameters for which C60 molecules can adopt a double helix configuration or a two-molecule layer configuration. Our calculations are performed within the framework of the bond-polarizability model combined with the spectral moment’s method. The changes in the Raman spectra as a function of C60 filling rate and the configuration of C60 molecules inside the nanotubes are identified and discussed. Our calculations support the experimental method proposed by Kuzmany to evaluate the concentration of C60 molecules inside nanotubes.

Deposition and Characterization of Diamond-Like Carbon Thin Films by Electro-Deposition Technique Using Organic Liquid

2004 ◽  
Vol 19 (4) ◽  
pp. 1126-1132 ◽  
Author(s):  
S.C. Ray ◽  
B. Bose ◽  
J.W. Chiou ◽  
H.M. Tsai ◽  
J.C. Jan ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Annealing Temperature ◽  
Organic Liquid ◽  
Ftir Spectra ◽  
Diamond Like Carbon ◽  
Deposition Technique ◽  
Spectra Analysis ◽  
Edge Structure ◽  
Electro Deposition ◽  
X Ray

Diamond-like carbon films were synthesized by electro-deposition technique from an organic liquid (a solution of alpha- and beta-pinenes in n-hexane) on silicon substrate at room temperature and at room pressure. The x-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectra, Raman spectra, photoluminescence (PL), and x-ray absorption near edge structure (XANES) spectra analysis were used to study the properties of the diamond-like carbon (as-deposited and annealed) films. The XRD measurement indicated that the film contains some diamond-crystalline phases whereas Raman spectra did not show any prominent diamond-like peak. PL intensity as higher for the as-deposited film and decreased with high-temperature vacuum annealing. FTIR spectra showed the presence of sp3 hybridization C–H bonds and their intensity decreases at higher annealing temperature. C and O K-edge XANES spectra showed that π* (sp2) intensity significantly decreases when the annealing temperature is 600 °C.

scholarly journals ImprovedEp-TL-LpDiagram and a Robust Regression Method

2011 ◽  
Vol 63 (4) ◽  
pp. 741-753 ◽  
Author(s):  
Ryo Tsutsui ◽  
Takashi Nakamura ◽  
Daisuke Yonetoku ◽  
Toshio Murakami ◽  
Yoshiyuki Morihara ◽  
...  
Keyword(s):  
Robust Regression ◽  
Regression Method

scholarly journals Selection and subsequent analysis of sib pair data for QTL detection

2001 ◽  
Vol 78 (2) ◽  
pp. 177-186 ◽  
Author(s):  
DIMITRIOS G. CHATZIPLIS ◽  
HENNING HAMANN ◽  
CHRIS S. HALEY
Keyword(s):  
Robust Regression ◽  
Expected Value ◽  
Regression Method ◽  
Qtl Detection ◽  
Selection Scheme ◽  
Phenotypic Differences ◽  
Effective Selection ◽  
The Difference ◽  
Trait Locus ◽  
Sib Pairs

Haseman and Elston (1972) developed a robust regression method for the detection of linkage between a marker and a quantitative trait locus (QTL) using sib pair data. The principle underlying this method is that the difference in phenotypes between pairs of sibs becomes larger as they share a decreasing number of alleles at a particular QTL identical by descent (IBD) from their parents. In this case, phenotypically very different sibs will also on average share a proportion of alleles IBD at any marker linked to the QTL that is lower than the expected value of 0·5. Thus, the deviation of the proportion of marker alleles IBD from the expected value in pairs of sibs selected to be phenotypically different (i.e. discordant) can provide a test for the presence of a QTL. A simple regression method for QTL detection in sib pairs selected for high phenotypic differences is presented here. The power of the analytical method was found to be greater than the power obtained using the standard analysis when samples of sib pairs with high phenotypic differences were used. However, the use of discordant sib pairs was found to be less powerful for QTL detection than alternative selective genotyping schemes based on the phenotypic values of the sibs except with intense selection, when its advantage was only marginal. The most effective selection scheme overall was the use of sib pairs from entire families selected on the basis of high within-family variance for the trait in question. There is little effect of selection on QTL position estimates, which are in good agreement with the simulated values. However, QTL variance estimates are biased to a greater or lesser degree, depending on the selection method.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

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