FUZZY LINEAR REGRESSION ANALYSIS FROM THE POINT OF VIEW RISK

2004 ◽  
Vol 12 (05) ◽  
pp. 635-649 ◽  
Author(s):  
MOHAMMAD MODARRES ◽  
EBRAHIM NASRABADI ◽  
MOHAMMAD MEHDI NASRABADI
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Fuzzy Numbers ◽  
Linear Regression Analysis ◽  
Point Of View ◽  
Risk Averse ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Risk Seeking ◽  
The Difference

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation.

A fuzzy multiple linear regression model based on meteorological factors for air quality index forecast

2021 ◽  
pp. 1-25
Author(s):  
Yujie Gu ◽  
Yuxiu Zhao ◽  
Jian Zhou ◽  
Hui Li ◽  
Yujie Wang
Keyword(s):  
Air Quality ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Quality Index ◽  
Fuzzy Numbers ◽  
Meteorological Factors ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression

Air quality index (AQI) is an indicator usually issued on a daily basis to inform the public how good or bad air quality recently is or how it will become over the next few days, which is of utmost importance in our life. To provide a more practicable way for AQI prediction, so that residents can clear about air conditions and make further plans, five imperative meteorological indicators are elaborately selected. Accordingly, taking these indicators as independent variables, a fuzzy multiple linear regression model with Gaussian fuzzy coefficients is proposed and reformulated, based on the linearity of Gaussian fuzzy numbers and Tanaka’s minimum fuzziness criterion. Subsequently, historical data in Shanghai from March 2016 to February 2018 are extracted from the government database and divided into two parts, where the first half is statistically analyzed and used for formulating four seasonal fuzzy linear regression models in views of the special climate environment of Shanghai, and the second half is used for prediction to validate the performance of the proposed model. Furthermore, considering that there is beyond dispute that triangular fuzzy number is more prevalent and crucial in the field of fuzzy studies for years, plenty of comparisons between the models based on the two types of fuzzy numbers are carried out by means of the three measures including the membership degree, the fuzziness and the credibility. The results demonstrate the powerful effectiveness and efficiency of the fuzzy linear regression models for AQI prediction, and the superiority of Gaussian fuzzy numbers over triangular fuzzy numbers in presenting the relationships between the meteorological factors and AQI.

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

Strategic Aspects of Numerosity Judgment: The Effect of Task Characteristics

2003 ◽  
Vol 50 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Koen Luwel ◽  
Lieven Verschaffel ◽  
Patrick Onghena ◽  
Erik De Corte
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Strategic Change ◽  
Strategy Use ◽  
Point Of View ◽  
Cognitive Tasks ◽  
Task Characteristics ◽  
Linear Regression Models ◽  
Grid Analysis ◽  
Numerosity Judgment

In previous investigations we documented that people use several strategies to determine different numerosities of blocks that are presented in a square grid. One of these strategies is the clever subtraction strategy, wherein the number of empty squares in the grid is subtracted from the total number of squares in the grid. In the present study we investigated participants’ flexibility in strategy use when varying the shape of the grid. Analysis of the results in terms of the theoretical framework of Lemaire and Siegler (1995 ) regarding strategic change shows that this contextual variable affected the frequency, execution time, and accuracy of subjects’ use of the subtraction strategy. The usefulness of this framework for analyzing the nature of the adaptation to contextual variations is discussed. From a methodological point of view, this study documents the potential of Beem’s (1993 , 1999) segmented linear regression models for assessing subjects’ strategy use in cognitive tasks.

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