scholarly journals Regression Diagnostic III: Autocorrelation

Econometrics ◽  
2015 ◽  
pp. 113-130
Author(s):  
Damodar Gujarati
2001 ◽  
Vol 30 (5) ◽  
pp. 799-813 ◽  
Author(s):  
M. Mercedes Suárez Rancel ◽  
Miguel A. González Sierra

Author(s):  
Edward F. Durner

Abstract This chapter focuses on regression diagnostics. The development of a regression equation is only the first half of a regression analysis. The second, often overlooked part of a regression analysis is to make sure the assumptions underlying the analysis have been met. This is easily accomplished using the regression diagnostic procedures available in SAS® (Statistical Analysis System). Price per flat of strawberries and their availability on the open market were used as an example.


Author(s):  
Nazaria Md Aris ◽  
Suzila Mohamed Yusof ◽  
Lim Jia Wen

Various theories and empirical studies have been applied and proposed to establish and explain how corporate governance practices are related to banks financial performance. This study concerns the relationship between corporate governance variables and bank performance in Malaysia. The data collected and analysed in this research is from quarter one year 2011 to quarter four year 2016. Various determinants have been identified namely return on equity(ROE) for bank performance measurement, CEO duality, board size, and board gender for corporate governance. Control variables are bank size and bank leverage. The methodologies adopted in this research includes descriptive analysis, correlation analysis, Pooled Ordinary Least Square (OLS) regression, Diagnostic Tests (Jarque-Bera Normality Test, Wooldridge Test and Variance Inflation Factor), Breusch-Pagan (BP) Lagrange Multiplier test, and Hausman test. In this study, the findings indicate that strong board composition and bank leverage were experience better performance.


Neurology ◽  
2018 ◽  
Vol 90 (12) ◽  
pp. e1038-e1046 ◽  
Author(s):  
David J. Irwin ◽  
Sharon X. Xie ◽  
David Coughlin ◽  
Naomi Nevler ◽  
Rizwan S. Akhtar ◽  
...  

ObjectiveTo test the association of antemortem CSF biomarkers with postmortem pathology in Lewy body disorders (LBD).MethodsPatients with autopsy-confirmed LBD (n = 24) and autopsy-confirmed Alzheimer disease (AD) (n = 23) and cognitively normal (n = 36) controls were studied. In LBD, neuropathologic criteria defined Lewy body α-synuclein (SYN) stages with medium/high AD copathology (SYN + AD = 10) and low/no AD copathology (SYN − AD = 14). Ordinal pathology scores for tau, β-amyloid (Aβ), and SYN pathology were averaged across 7 cortical regions to obtain a global cerebral score for each pathology. CSF total tau (t-tau), phosphorylated tau at threonine181, and Aβ1-42 levels were compared between LBD and control groups and correlated with global cerebral pathology scores in LBD with linear regression. Diagnostic accuracy for postmortem categorization of LBD into SYN + AD vs SYN − AD or neocortical vs brainstem/limbic SYN stage was tested with receiver operating curves.ResultsSYN + AD had higher CSF t-tau (mean difference 27.0 ± 8.6 pg/mL) and lower Aβ1-42 (mean difference −84.0 ± 22.9 g/mL) compared to SYN − AD (p < 0.01, both). Increasing global cerebral tau and plaque scores were associated with higher CSF t-tau (R2 = 0.15–0.16, p < 0.05, both) and lower Aβ1-42 (R2 = 0.43–0.49, p < 0.001, both), while increasing cerebral SYN scores were associated with lower CSF Aβ1-42 (R2 = 0.31, p < 0.001) and higher CSF t-tau/Aβ1-42 ratio (R2 = 0.27, p = 0.01). CSF t-tau/Aβ1-42 ratio had 100% specificity and 90% sensitivity for SYN + AD, and CSF Aβ1-42 had 77% specificity and 82% sensitivity for neocortical SYN stage.ConclusionsHigher antemortem CSF t-tau/Aβ1-42 and lower Aβ1-42 levels are predictive of increasing cerebral AD and SYN pathology. These biomarkers may identify patients with LBD vulnerable to cortical SYN pathology who may benefit from both SYN and AD-targeted disease-modifying therapies.


Eye ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 326-333
Author(s):  
Ruyi Zhai ◽  
Jingyi Cheng ◽  
Huan Xu ◽  
Zhaobin Fang ◽  
Xu Chen ◽  
...  

Abstract Background Intraocular pressure (IOP) is important in the pathogenesis of glaucoma and its circadian fluctuations are important in the disease management; however, there are no adequate parameters to describe the fluctuations. This study investigates a new parameter, mean amplitude of intraocular pressure excursion (MAPE), and compares its ability in assessing 24-h IOP fluctuations with other ocular parameters. Methods Only the right eye was evaluated in each of the 79 healthy people and 164 untreated patients with primary open angle glaucoma (POAG). Each participant underwent 24-h IOP monitoring by measuring IOP every 2 h. IOP fluctuations were expressed as MAPE calculations and currently used parameters included mean IOP, standard deviation of IOP, max difference and area under the circadian IOP curve. Comprehensive ophthalmologic examinations were also performed. Associations between visual field deficits and IOP fluctuation parameters were investigated via partial least squares (PLS) regression. Diagnostic performance was evaluated with area under the receiver operating characteristic curves (ROC). Results Compared with healthy volunteers, the MAPE values in POAG patients were higher (4.16 ± 1.90 versus 2.45 ± 0.89, p < 0.01). In PLS regressions where visual field deficits were as dependent variable, MAPE had the highest score regarding variable importance in projection, and its standard regression coefficient was larger than other parameters. Diagnostic performance analysis showed the area under ROC of MAPE for glaucoma detection was 0.822 (0.768–0.868, p < 0.001). Conclusions MAPE might be an effective parameter in clinic to characterise IOP circadian fluctuations.


Least squares minimization is by nature global and, hence, vulnerable to distortion by outliers. We present a novel technique to reject outliers from an m -dimensional data set when the underlying model is a hyperplane (a line in two dimensions, a plane in three dimensions). The technique has a sound statistical basis and assumes that Gaussian noise corrupts the otherwise valid data. The majority of alternative techniques available in the literature focus on ordinary least squares , where a single variable is designated to be dependent on all others - a model that is often unsuitable in practice. The method presented here operates in the more general framework of orthogonal regression , and uses a new regression diagnostic based on eigendecomposition. It subsumes the traditional residuals scheme and, using matrix perturbation theory, provides an error model for the solution once the contaminants have been removed.


Technometrics ◽  
1998 ◽  
Vol 40 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Kenneth N. Berk

Sign in / Sign up

Export Citation Format

Share Document