Exploration, Exploitation Phenomena and Regression Analysis: Propensity Metric, Anomaly Reduction, Dimensionality Reduction

Polibits ◽  
2018 ◽  
Vol 57 ◽  
pp. 19-37
Author(s):  
Chaman Lal Sabharwal
Keyword(s):  
Regression Analysis ◽  
Dimensionality Reduction ◽  
Exploration Exploitation

Unsupervised, Supervised and Semi‐supervised Dimensionality Reduction by Low‐Rank Regression Analysis

2021 ◽  
Vol 30 (4) ◽  
pp. 603-610
Author(s):  
TANG Kewei ◽  
ZHANG Jun ◽  
ZHANG Changsheng ◽  
WANG Lijun ◽  
ZHAI Yun ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Dimensionality Reduction ◽  
Low Rank ◽  
Rank Regression

scholarly journals Burr removal from measurement data of honeycomb core surface based on dimensionality reduction and regression analysis

2018 ◽  
Vol 29 (11) ◽  
pp. 115010 ◽  
Author(s):  
Yan Qin ◽  
Renke Kang ◽  
Zhigang Dong ◽  
Yidan Wang ◽  
Jie Yang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Dimensionality Reduction ◽  
Measurement Data ◽  
Honeycomb Core ◽  
Core Surface

XRCC1 632 as a candidate for cancer predisposition via a complex interaction with genetic variants of base excision repair and double strand break repair genes

2019 ◽  
Vol 15 (33) ◽  
pp. 3845-3859 ◽  
Author(s):  
Amrita Singh ◽  
Navneet Singh ◽  
Digambar Behera ◽  
Siddharth Sharma
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Dimensionality Reduction ◽  
Odds Ratio ◽  
Multifactor Dimensionality Reduction ◽  
Excision Repair ◽  
Five Factor Model ◽  
Genetic Alterations ◽  
Repair System ◽  
Multifactor Dimensionality Reduction Analysis

Aim: The DNA repair system safeguards integrity of DNA. Genetic alterations force the improper repair which in conjugation with other factors ultimately results in carcinogenesis. Materials & methods: PCR-restriction fragment length polymorphism was used for genotyping, which was followed by statistical analysis using logistic regression analysis, multifactor dimensionality reduction and classification and regression analysis tree, elaborating the association with lung cancer subjects. Results: Combination of XRCC1 632 and OGG1326 showcased a high risk of eightfold (odds ratio: 7.92; 95% CI: 2.68–23.4; p = 0.0002; false discovery rate (FDR) p = 0.002). Similarly, XRCC1 632 and MUTYH 324 (odds ratio: 5.07; 95% CI: 2.6–9.67; p < 0.0001; FDRp = 0.002) had a high risk. Multifactor dimensionality reduction analysis revealed five factor model as the best model with prediction error of 0.37 (p = 0.02). Conclusion: There was a clear indication that high order interactions were major role players in the study.

scholarly journals Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Zhang Zhongya ◽  
Jin Xiaoguang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Dimensionality Reduction ◽  
Particle Velocity ◽  
Principal Components ◽  
Common Factors ◽  
Output Parameter ◽  
Blasting Vibration ◽  
Ann Models

Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

Regression Analysis of Count Data

1998 ◽  
Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi
Keyword(s):  
Regression Analysis ◽  
Count Data

Poor Acceptance of the Past is Related to Depressive Symptoms in Older Adults

GeroPsych ◽  
2020 ◽  
Vol 33 (4) ◽  
pp. 246-251
Author(s):  
Gozde Cetinkol ◽  
Gulbahar Bastug ◽  
E. Tugba Ozel Kizil
Keyword(s):  
Older Adults ◽  
Regression Analysis ◽  
Depressive Symptoms ◽  
Death Anxiety ◽  
Geriatric Depression ◽  
Ego Integrity ◽  
The Past ◽  
Depressed Group ◽  
Total Death ◽  
The Relationship

Abstract. Depression in older adults can be explained by Erikson’s theory on the conflict of ego integrity versus hopelessness. The study investigated the relationship between past acceptance, hopelessness, death anxiety, and depressive symptoms in 100 older (≥50 years) adults. The total Beck Hopelessness (BHS), Geriatric Depression (GDS), and Accepting the Past (ACPAST) subscale scores of the depressed group were higher, while the total Death Anxiety (DAS) and Reminiscing the Past (REM) subscale scores of both groups were similar. A regression analysis revealed that the BHS, DAS, and ACPAST predicted the GDS. Past acceptance seems to be important for ego integrity in older adults.

Measures of RSA Suppression, Attentional Control, and Negative Affect Predict Self-Ratings of Executive Functions

2011 ◽  
Vol 25 (4) ◽  
pp. 164-173 ◽  
Author(s):  
Brian Healy ◽  
Aaron Treadwell ◽  
Mandy Reagan
Keyword(s):  
College Students ◽  
Heart Rate ◽  
Regression Analysis ◽  
Individual Differences ◽  
Negative Affect ◽  
Respiratory Sinus Arrhythmia ◽  
Attentional Control ◽  
Executive Processes ◽  
Sinus Arrhythmia ◽  
Low Levels

The current study was an attempt to determine the degree to which the suppression of respiratory sinus arrhythmia (RSA) and attentional control were influential in the ability to engage various executive processes under high and low levels of negative affect. Ninety-four college students completed the Stroop Test while heart rate was being recorded. Estimates of the suppression of RSA were calculated from each participant in response to this test. The participants then completed self-ratings of attentional control, negative affect, and executive functioning. Regression analysis indicated that individual differences in estimates of the suppression of RSA, and ratings of attentional control were associated with the ability to employ executive processes but only when self-ratings of negative affect were low. An increase in negative affect compromised the ability to employ these strategies in the majority of participants. The data also suggest that high attentional control in conjunction with attenuated estimates of RSA suppression may increase the ability to use executive processes as negative affect increases.

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