Correlation, Method of Least Squares, Linear Regression and the General Linear Model

2014 ◽  
pp. 105-138
Author(s):  
Frits Agterberg
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Model ◽  
Correlation Method ◽  
General Linear Model ◽  
General Linear ◽  
Method Of Least Squares

scholarly journals Least Squares Estimations for the General Linear Model Parameters with Epsilon Skew Normal Error Term

2020 ◽  
pp. 636-645
Author(s):  
Hussain Karim Nashoor ◽  
Ebtisam Karim Abdulah
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Least Squares Method ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
Skewed Distribution ◽  
General Linear ◽  
Average Error ◽  
Model Parameters ◽  
Skew Normal

Examination of skewness makes academics more aware of the importance of accurate statistical analysis. Undoubtedly, most phenomena contain a certain percentage of skewness which resulted to the appearance of what is -called "asymmetry" and, consequently, the importance of the skew normal family . The epsilon skew normal distribution ESN (μ, σ, ε) is one of the probability distributions which provide a more flexible model because the skewness parameter provides the possibility to fluctuate from normal to skewed distribution. Theoretically, the estimation of linear regression model parameters, with an average error value that is not zero, is considered a major challenge due to having difficulties, as no explicit formula to calculate these estimates can be obtained. Practically, values for these estimates can be obtained only by referring to numerical methods. This research paper is dedicated to estimate parameters of the Epsilon Skew Normal General Linear Model (ESNGLM) using an adaptive least squares method, as along with the employment of the ordinary least squares method for estimating parameters of the General Linear Model (GLM). In addition, the coefficient of determination was used as a criterion to compare the models’ preference. These methods were applied to real data represented by dollar exchange rates. The Matlab software was applied in this work and the results showed that the ESNGLM represents a satisfactory model. 

The use of MIXED models in the analysis of animal experiments with repeated measures data

2004 ◽  
Vol 84 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Z. Wang and L. A. Goonewardene
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Repeated Measures ◽  
Mixed Model ◽  
Covariance Structure ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
General Linear ◽  
Repeated Measures Data ◽  
Split Plot

The analysis of data containing repeated observations measured on animals (experimental unit) allocated to different treatments over time is a common design in animal science. Conventionally, repeated measures data were either analyzed as a univariate (split-plot in time) or a multivariate ANOVA (analysis of contrasts), both being handled by the General Linear Model procedure of SAS. In recent times, the mixed model has become more appealing for analyzing repeated data. The objective of this paper is to provide a background understanding of mixed model methodology in a repeated measures analysis and to use balanced steer data from a growth study to illustrate the use of PROC MIXED in the SAS system using five covariance structures. The split-plot in time approach assumes a constant variance and equal correlations (covariance) between repeated measures or compound symmetry, regardless of their proximity in time, and often these assumptions are not true. Recognizing this limitation, the analysis of contrasts was proposed. If there are missing measurements, or some of the data are measured at different times, such data were excluded resulting in inadequate data for a meaningful analysis. The mixed model uses the generalized least squares method, which is generally better than the ordinary least squares used by GLM, if the appropriate covariance structure is adopted. The presence of unequally spaced and/or missing data does not pose a problem for the mixed model. In the example analyzed, the first order ante dependence [ANTE(1)] covariance model had the lowest value for the Akaike and Schwarz’s Bayesian information criteria fit statistics and is therefore the model that provided the best fit to our data. Hence, F values, least square estimates and standard errors based on the ANTE (1) were considered the most appropriate from among the five models demonstrated. It is recommended that the mixed model be used for the analysis of repeated measures designs in animal studies. Key words: Repeated measures, General Linear Model, Mixed Model, split-plot, covariance structure

scholarly journals Sensitivity analysis in linear models

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Shuangzhe Liu ◽  
Tiefeng Ma ◽  
Yonghui Liu
Keyword(s):  
Sensitivity Analysis ◽  
Least Squares ◽  
Linear Model ◽  
Linear Models ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
The Other ◽  
Regression Coefficients ◽  
General Linear ◽  
Least Squares Estimators

AbstractIn this work, we consider the general linear model or its variants with the ordinary least squares, generalised least squares or restricted least squares estimators of the regression coefficients and variance. We propose a newly unified set of definitions for local sensitivity for both situations, one for the estimators of the regression coefficients, and the other for the estimators of the variance. Based on these definitions, we present the estimators’ sensitivity results.We include brief remarks on possible links of these definitions and sensitivity results to local influence and other existing results.

scholarly journals Association among Soluble Tumor Necrosis Factor Receptor I, Matrix Metalloproteinase-9, Procollagen C-terminal Propeptide type I, and N-Terminal proBrain Natriuretic Peptide in Heart Failure

2011 ◽  
Vol 3 (1) ◽  
pp. 64
Author(s):  
Mona Yolanda ◽  
Marsetio Donosepoetro ◽  
Anwar Santoso ◽  
Mansyur Arif
Keyword(s):  
Heart Failure ◽  
Tumor Necrosis Factor ◽  
Linear Regression ◽  
Path Analysis ◽  
Linear Model ◽  
Tumor Necrosis ◽  
Tumor Necrosis Factor Receptor ◽  
General Linear Model ◽  
General Linear ◽  
Necrosis Factor

BACKGROUND: Heart failure (HF) is associated with an increased expression of proinflammatory cytokines, especially soluble tumor necrosis factor receptor I (sTNFR I), but the underlying mechanism to the relationship between sTNFR I activation and the progression of HF is not yet fully understood. This study aims to see the association between sTNFR I, MMP-9, PICP, and NT-proBNP in the progression of HF.METHODS: This was a cross sectional study which recruited 45 subjects with HF confirmed by echocardiography and NT-proBNP. Concentration sTNFR I, MMP-9, and PICP were measured using ELISA method, whereas NT-proBNP concentration was measured using ECLIA method. Univariate linear regression analysis, path analysis and General Linear Model were used to determine which parameters played the most significant role in HF.RESULTS: Results of the univariate linear regression and path analysis showed there was a linear relationship between sTNFR I with MMP-9, with R square of 25.8% (p=0.00; r=0.508), R square sTNFRI and MMP-9 with PICP was 14.4% (p=0.038; r=0.379) and R square MMP-9 and PICP with NT-proBNP was 39.6% (p=0.00; r=0.629). From the General Linear Model we found that the important predictor for HF was through MMP-9 and PICP.CONCLUSION: sTNFR I as a proinflammatory factor is one of the factors involved in the heart failure as seen by NT-proBNP through activation of brosis (PICP) and remodeling factor (MMP-9).KEYWORDS: sTNFR I, MMP-9, PICP, NT-proBNP, heart failure

EEG-fMRI: Including sleep-induced potentials in the general linear model improves spatial accordance with epileptic focus

2010 ◽  
Vol 41 (02) ◽  
Author(s):  
J Möhring ◽  
D Coropceanu ◽  
F Möller ◽  
S Wolff ◽  
R Boor ◽  
...  
Keyword(s):  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Epileptic Focus
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