p-Value adjustment to control type I errors in linear regression models

2017 ◽  
Vol 87 (9) ◽  
pp. 1701-1711 ◽  
Author(s):  
Nikita A. Moiseev
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
P Value ◽  
Type I ◽  
Linear Regression Models ◽  
Type I Errors

scholarly journals Reduced left atrial strain in magnetic resonance imaging is associated with abnormal P-waves in patients after acute myocardial infarction

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Wester ◽  
J Pec ◽  
C Fisser ◽  
K Debl ◽  
O Hamer ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Linear Regression ◽  
Left Atrial ◽  
Regression Models ◽  
Linear Regression Analysis ◽  
P Wave ◽  
P Value ◽  
Linear Regression Models ◽  
The Relationship

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): ReForM-B-Program Background Abnormal P-wave terminal force in lead V1 (PTFV1) is associated with atrial remodeling. The relationship between PTFV1 and atrial function after acute myocardial injury is insufficiently understood and may be elucidated by detailed feature tracking (FT) strain analysis of cardiac magnetic resonance images (CMR). Purpose We investigated the relationship between PTFV1 and left atrial (LA) strain (measured by CMR) in a patient cohort presenting with acute myocardial infarction (MI). Methods 56 patients with acute MI underwent CMR within 3-5 days after MI. PTFV1 was measured as the product of negative P-wave amplitude and duration in lead V1 (Fig. A). A PTFV1 >4000 ms*µV was defined as abnormal. CMR cine data were retrospectively analyzed using a dedicated FT software. LA strain (ε) and strain rate (SR) for atrial reservoir ([εs]; [SRs]), conduit ([εe]; [SRe]) and booster function ([εa]; [SRa]) were measured in two long-axis views (Fig. A). Results Patients with abnormal PTFV1 had significantly reduced LA conduit function εe and SRe (Fig. B + D). There was a significant negative correlation between the extent of PTFV1 and both εe and SRe (Fig. C + E). In univariate and multivariate regression models, both PTFV1 and age predicted atrial conduit function. In contrast, multiple clinical co-factors had no significant influence on εe (Table). Interestingly, linear regression models revealed only mild dependency of PTFV1 on conventional parameters of cardiac function such as left ventricular ejection fraction (p = 0.059; R²(adj.)=0.047), and no dependency on structural parameters such as LA area (p = 0.639; R²(adj.)=0.016), or LA fractional area change (p = 0.825; R²(adj.)=0.020). Conclusion Abnormal PTFV1 was associated with reduced LA function independent from numerous clinical co-factors in patients presenting with acute myocardial infarction. Table N = 56 Linear Regression Analysis Multiple Linear Regression Analysis (R2 (adj.)=0.376, p = 0.016) Variable B 95% CI P value R2 (adj.) B 95% CI P value PTFV1 [µV*ms] -1.628 17085.298 to 27210.854 0.013 0.092 -1.315 -2.614 to -0.016 0.047 Age [y] -425.775 24985.168 to 54634.995 0.002 0.145 -610.815 -982.78 to -238.849 0.001 Body mass indes [kg/m2] -185.653 -3259.187 to 47020.775 0.671 -0.015 -506.096 -1327.357 to 315.165 0.219 Creatinine kinase [U/l] -1.571 14806.991 to 24842.272 0.121 0.027 -1.791 -3.72 to 0.138 0.067 Male sex -893.28 10701.206 to 23504.066 0.802 -0.017 4275.631 -3842.517 to 12393.78 0.292 Estimated glomerular filtration rate [ml/min/1.73m2] 88.617 -4564.177 to 21395.361 0.202 0.012 -163.981 -331.343 to 3.381 0.054 Systolic blood pressure [mmHg] -2.001 14045.786 to 22037.253 0.095 0.038 29.331 -108.243 to 166.906 0.668 nt-pro brain natriuretic peptide [pg/ml] 24.629 -4060.804 to 30920.828 0.716 -0.016 1.015 -1.778 to 3.809 0.466 Univariate and multivariate linear regression models for left atrial conduit strain Abstract Figure

scholarly journals empirical likelihood ratio based comparative study on tests for normality of residuals in linear models

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Chioneso Marange ◽  
Yongsong Qin
Keyword(s):  
Monte Carlo ◽  
Linear Regression ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Simple Linear Regression ◽  
Linear Regression Models

The application of goodness-of-fit (GoF) tests in linear regression modeling is a common practice in applied statistical sciences. For instance, in simple linear regression the assumption of normality of residuals is always necessary to test before making any further inferences. The growing popularity of the use of powerful and efficient empirical likelihood ratio (ELR) based GoF tests in checking for departures from normality in various continuous distributions can be of great use in checking for distributional assumptions of residuals in linear models. Motivated by the attractive properties of the ELR based GoF tests the researchers conducted an extensive Type I error rate assessment as well as a Monte Carlo power comparison of selected ELR GoF tests with well-known existing tests against symmetric and asymmetric alternative OLS and BLUS residuals. Under the simulated scenarios, all the studied tests have good control of Type I error rates. The Monte Carlo experiments revealed the superiority of the ELR GoF tests under certain alternatives of both the OLS and BLUS residuals. Our findings also demonstrated the superiority of OLS over BLUS residuals when one is testing for normality in simple linear regression models. A real data study further revealed the applicability of the ELR based GoF tests in testing normality of residuals in linear regression models.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

Sign in / Sign up

Export Citation Format

Share Document