scholarly journals Method for Solving LASSO Problem Based on Multidimensional Weight

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Chen ChunRong ◽  
Chen ShanXiong ◽  
Chen Lin ◽  
Zhu YuChen
Keyword(s):  
Stepwise Regression ◽  
Principal Component ◽  
Threshold Value ◽  
Information Criteria ◽  
Pima Indians ◽  
Least Angle Regression ◽  
Lasso Problem ◽  
Selection Operator ◽  
Excellent Property ◽  
The Impact

In the data mining, the analysis of high-dimensional data is a critical but thorny research topic. The LASSO (least absolute shrinkage and selection operator) algorithm avoids the limitations, which generally employ stepwise regression with information criteria to choose the optimal model, existing in traditional methods. The improved-LARS (Least Angle Regression) algorithm solves the LASSO effectively. This paper presents an improved-LARS algorithm, which is constructed on the basis of multidimensional weight and intends to solve the problems in LASSO. Specifically, in order to distinguish the impact of each variable in the regression, we have separately introduced part of principal component analysis (Part_PCA), Independent Weight evaluation, and CRITIC, into our proposal. We have explored that these methods supported by our proposal change the regression track by weighted every individual, to optimize the approach direction, as well as the approach variable selection. As a consequence, our proposed algorithm can yield better results in the promise direction. Furthermore, we have illustrated the excellent property of LARS algorithm based on multidimensional weight by the Pima Indians Diabetes. The experiment results show an attractive performance improvement resulting from the proposed method, compared with the improved-LARS, when they are subjected to the same threshold value.

PP46 When Composite Measures Or Indices Fail: Data Processing Lessons

2018 ◽  
Vol 34 (S1) ◽  
pp. 83-83
Author(s):  
Yi-Sheng Chao ◽  
Chao-Jung Wu
Keyword(s):  
Stepwise Regression ◽  
Principal Component ◽  
Composite Measures ◽  
Data Limitations ◽  
Common Causes ◽  
Different Types ◽  
Forward Stepwise ◽  
The Creation ◽  
Input Variables ◽  
The Impact

Introduction:Index mining is a new discipline that aims to search for the composite measures or indices most relevant to the contexts or outcomes. After reviewing three frailty indices and principal component (PC)-based indices, we hereby show certain occasions that can lead to ineffective indices, which consist of bias or fail to represent the theories.Methods:We reproduced and reviewed the three frailty indices and the 134,689 PC (principal component) -based indices from previous publications. The impact of aggregating the input variables on the final indices was analyzed using forward stepwise regression.Results:Several methods to combine the input variables were related to ineffective projection of information onto the indices. The most common causes leading to ineffective summation of input variables were shown in three examples involving different types of input variables, which were positively or negatively correlated or uncorrelated to the outcome. Ineffective indices were created often because of the summation of redundant information or uncorrelated variables.Conclusions:The creation of ineffective indices can be avoided if the relationships between input variables and outcomes are properly scrutinized. The creation of composite measures and indices is still a discipline under active development. The three examples we identified are the mistakes that may be repeated unintentionally and need to be addressed with explicit rules. A reporting guide for the creation of composite measures has been proposed. A proper review of index objectives, data characteristics, and data limitations before creating composite measures or indices is recommended.

The Impact of Shariah Governance and Corporate Governance on the Risk Management Practices: Evidence from Local and Foreign Islamic Banks in Malaysia

2018 ◽  
Vol 15 (2) ◽  
pp. 1-20
Author(s):  
Sabri Embi ◽  
Zurina Shafii
Keyword(s):  
Risk Management ◽  
Corporate Governance ◽  
Management Practices ◽  
Principal Component ◽  
T Test ◽  
The Body ◽  
Primary Data ◽  
Islamic Banks ◽  
Shariah Governance ◽  
The Impact

The purpose of this study is to examine the impact of Shariah governance and corporate governance (CG) on the risk management practices (RMPs) of local Islamic banks and foreign Islamic banks operating in Malaysia. The Shariah governance comprises the Shariah review (SR) and Shariah audit (SA) variables. The study also evaluates the level of RMPs, CG, SR, and SA between these two type of banks. With the aid of SPSS version 20, the items for RMPs, CG, SR, and SA were subjected to principal component analysis (PCA). From the PCA, one component or factor was extracted each for the CG, SR, and RMPs while another two factors were extracted for the SA. Primary data was collected using a self-administered survey questionnaire. The questionnaire covers four aspects ; CG, SR, SA, and RMPs. The data received from the 300 usable questionnaires were subjected to correlation and regression analyses as well as an independent t-test. The result of correlation analysis shows that all the four variables have large positive correlations with each other indicating a strong and significant relationship between them. From the regression analysis undertaken, CG, SR, and SA together explained 52.3 percent of the RMPs and CG emerged as the most influential variable that impacts the RMPs. The independent t-test carried out shows that there were significant differences in the CG and SA between the local and foreign Islamic banks. However, there were no significant differences between the two types of the bank in relation to SR and RMPs. The study has contributed to the body of knowledge and is beneficial to academicians, industry players, regulators, and other stakeholders.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals Inheritable testicular metabolic memory of high-fat diet causes transgenerational sperm defects in mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luís Crisóstomo ◽  
Ivana Jarak ◽  
Luís P. Rato ◽  
João F. Raposo ◽  
Rachel L. Batterham ◽  
...  
Keyword(s):  
Sperm Quality ◽  
Feeding Habits ◽  
Reproductive Function ◽  
Principal Component ◽  
Testicular Tissue ◽  
Sperm Parameters ◽  
Metabolic Memory ◽  
High Fat ◽  
And Function ◽  
The Impact

AbstractThe consumption of energy-dense diets has contributed to an increase in the prevalence of obesity and its comorbidities worldwide. The adoption of unhealthy feeding habits often occurs at early age, prompting the early onset of metabolic disease with unknown consequences for reproductive function later in life. Recently, evidence has emerged regarding the intergenerational and transgenerational effects of high-fat diets (HFD) on sperm parameters and testicular metabolism. Hereby, we study the impact of high-fat feeding male mice (F0) on the testicular metabolome and function of their sons (F1) and grandsons (F2). Testicular content of metabolites related to insulin resistance, cell membrane remodeling, nutritional support and antioxidative stress (leucine, acetate, glycine, glutamine, inosine) were altered in sons and grandsons of mice fed with HFD, comparing to descendants of chow-fed mice. Sperm counts were lower in the grandsons of mice fed with HFD, even if transient. Sperm quality was correlated to testicular metabolite content in all generations. Principal Component Analysis of sperm parameters and testicular metabolites revealed an HFD-related phenotype, especially in the diet-challenged generation and their grandsons. Ancestral HFD, even if transient, causes transgenerational “inherited metabolic memory” in the testicular tissue, characterized by changes in testicular metabolome and function.

scholarly journals The Effect of Fat Content and Fatty Acids Composition on Color and Textural Properties of Butter

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4565
Author(s):  
Sergiu Pădureţ
Keyword(s):  
Fatty Acids ◽  
Principal Component ◽  
Textural Properties ◽  
Fat Content ◽  
Fatty Acids Composition ◽  
Color Parameter ◽  
Butter Fat ◽  
Composition Variation ◽  
Texture Parameters ◽  
The Impact

The textural properties of butter are influenced by its fat content and implicitly by the fatty acids composition. The impact of butter’s chemical composition variation was studied in accordance with texture and color properties. From 37 fatty acids examined, only 18 were quantified in the analyzed butter fat samples, and approximately 69.120% were saturated, 25.482% were monounsaturated, and 5.301% were polyunsaturated. The butter samples’ viscosity ranged between 0.24 and 2.12 N, while the adhesiveness ranged between 0.286 to 18.19 N·mm. The principal component analysis (PCA) separated the butter samples based on texture parameters, fatty acids concentration, and fat content, which were in contrast with water content. Of the measured color parameters, the yellowness b* color parameter is a relevant indicator that differentiated the analyzed sample into seven statistical groups; the ANOVA statistics highlighted this difference at a level of p < 0.001.

scholarly journals Exploring Residents’ Perceptions of the Socio-Cultural Benefits of Tourism Development in the Mountain Area

Societies ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 83
Author(s):  
Iulia C. Muresan ◽  
Rezhen Harun ◽  
Felix H. Arion ◽  
Ava Omar Fatah ◽  
Diana E. Dumitras
Keyword(s):  
Positive Attitude ◽  
Statistical Tests ◽  
Tourism Development ◽  
Principal Component ◽  
Local Communities ◽  
Three Dimensions ◽  
Development Economic ◽  
Mountain Area ◽  
Sustainable Tourism Development ◽  
The Impact

Development of tourism affected the socio-cultural environment of many destinations. Previous studies have focused more on analyzing the impact of tourism on all three dimensions of sustainable development (economic, environment and socio-cultural); therefore, the present paper examines tourism development’s impact with regard to the socio-cultural benefits that enhance sustainable tourism development. A survey based on a questionnaire was employed in June 2018 in a mountain village in Cluj County, Romania. The collected data were analyzed using principal component analysis, and several statistical tests were conducted. The results indicated that the respondents have a positive attitude towards tourism development and socio-cultural perceived benefits. Older people and those running a business tend to perceive more positively the benefits of tourism development. The findings of the research could contribute to future development strategies, as it is well known that supporting local communities influences the success of tourism destination.

scholarly journals Grill Workers Exposure to Polycyclic Aromatic Hydrocarbons: Levels and Excretion Profiles of the Urinary Biomarkers

2020 ◽  
Vol 18 (1) ◽  
pp. 230
Author(s):  
Marta Oliveira ◽  
Sílvia Capelas ◽  
Cristina Delerue-Matos ◽  
Simone Morais
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
High Performance ◽  
Principal Component ◽  
Fold Increase ◽  
Urinary Biomarkers ◽  
Analysis Model ◽  
Exposed Workers ◽  
Polycyclic Aromatic ◽  
The Impact

Grilling activities release large amounts of hazardous pollutants, but information on restaurant grill workers’ exposure to polycyclic aromatic hydrocarbons (PAHs) is almost inexistent. This study assessed the impact of grilling emissions on total workers’ exposure to PAHs by evaluating the concentrations of six urinary biomarkers of exposure (OHPAHs): naphthalene, acenaphthene, fluorene, phenanthrene, pyrene, and benzo(a)pyrene. Individual levels and excretion profiles of urinary OHPAHs were determined during working and nonworking periods. Urinary OHPAHs were quantified by high-performance liquid-chromatography with fluorescence detection. Levels of total OHPAHs (∑OHPAHs) were significantly increased (about nine times; p ≤ 0.001) during working comparatively with nonworking days. Urinary 1-hydroxynaphthalene + 1-hydroxyacenapthene and 2-hydroxyfluorene presented the highest increments (ca. 23- and 6-fold increase, respectively), followed by 1-hydroxyphenanthrene (ca. 2.3 times) and 1-hydroxypyrene (ca. 1.8 times). Additionally, 1-hydroxypyrene levels were higher than the benchmark, 0.5 µmol/mol creatinine, in 5% of exposed workers. Moreover, 3-hydroxybenzo(a)pyrene, biomarker of exposure to carcinogenic PAHs, was detected in 13% of exposed workers. Individual excretion profiles showed a cumulative increase in ∑OHPAHs during consecutive working days. A principal component analysis model partially discriminated workers’ exposure during working and nonworking periods showing the impact of grilling activities. Urinary OHPAHs were increased in grill workers during working days.

scholarly journals Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

2021 ◽  
Vol 13 (10) ◽  
pp. 5359
Author(s):  
Afrika Onguko Okello ◽  
Jonathan Makau Nzuma ◽  
David Jakinda Otieno ◽  
Michael Kidoido ◽  
Chrysantus Mbi Tanga
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Principal Component ◽  
Farm Income ◽  
Purchase Decisions ◽  
Wealth Status ◽  
Feed Formulation ◽  
Alternative Protein ◽  
Livestock Products ◽  
The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

scholarly journals A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

2021 ◽  
Vol 13 (10) ◽  
pp. 5608
Author(s):  
Manjiang Shi ◽  
Qi Cao ◽  
Baisong Ran ◽  
Lanyan Wei
Keyword(s):  
Disaster Recovery ◽  
Social Process ◽  
Principal Component ◽  
Assessment Method ◽  
International Consensus ◽  
Long Term Stability ◽  
The People ◽  
Policy Guidance ◽  
The Impact ◽  
Post Disaster

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

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