Improvement of Prediction Ability of Multicomponent Regression Model

2008 ◽  
Author(s):  
Ling Gao ◽  
Shouxin Ren
Keyword(s):  
Regression Model ◽  
Prediction Ability

scholarly journals Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

2021 ◽  
Vol 9 ◽  
Author(s):  
Deliang Sun ◽  
Haijia Wen ◽  
Jiahui Xu ◽  
Yalan Zhang ◽  
Danzhou Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Dominant Factor ◽  
Mountainous Area ◽  
Prediction Ability ◽  
Before And After ◽  
Fold Cross Validation

This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

scholarly journals Logistic Regression Model Using Scheimpflug-Placido Cornea Topographer Parameters to Diagnose Keratoconus

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Emre Altinkurt ◽  
Ozkan Avci ◽  
Orkun Muftuoglu ◽  
Adem Ugurlu ◽  
Zafer Cebeci ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Roc Curve ◽  
Logistic Regression Model ◽  
Corneal Thickness ◽  
Computer Algorithms ◽  
Roc Curve Analysis ◽  
Data Set ◽  
Prediction Ability ◽  
Keratorefractive Surgery

Purpose. Diagnose keratoconus by establishing an effective logistic regression model from the data obtained with a Scheimpflug-Placido cornea topographer. Methods. Topographical parameters of 125 eyes of 70 patients diagnosed with keratoconus by clinical or topographical findings were compared with 120 eyes of 63 patients who were defined as keratorefractive surgery candidates. The receiver operating character (ROC) curve analysis was performed to determine the diagnostic ability of the topographic parameters. The data set of parameters with an AUROC (area under the ROC curve) value greater than 0.9 was analyzed with logistic regression analysis (LRA) to determine the most predictive model that could diagnose keratoconus. A logit formula of the model was built, and the logit values of every eye in the study were calculated according to this formula. Then, an ROC analysis of the logit values was done. Results. Baiocchi Calossi Versaci front index (BCVf) had the highest AUROC value (0.976) in the study. The LRA model, which had the highest prediction ability, had 97.5% accuracy, 96.8% sensitivity, and 99.2% specificity. The most significant parameters were found to be BCVf ( p = 0.001 ), BCVb (Baiocchi Calossi Versaci back) ( p = 0.002 ), posterior rf (apical radius of the flattest meridian of the aspherotoric surface in 4.5 mm diameter of the cornea) ( p = 0.005 ), central corneal thickness ( p = 0.072 ), and minimum corneal thickness ( p = 0.494 ). Conclusions. The LRA model can distinguish keratoconus corneas from normal ones with high accuracy without the need for complex computer algorithms.

PLS Regression on Coal Infrared Spectrum with Wavelet Pre-Processing

2011 ◽  
Vol 80-81 ◽  
pp. 279-283
Author(s):  
Yan Ming Wang ◽  
Gou Qing Shi ◽  
Xiao Xing Zhong ◽  
De Ming Wang
Keyword(s):  
Infrared Spectrum ◽  
Regression Model ◽  
Multivariate Calibration ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Data Set ◽  
Prediction Ability ◽  
Data Regression ◽  
Preprocessing Technique ◽  
Compressed Data

Study on multivariate calibration for infrared spectrum of coal was presented. The discrete wavelet transformation as pre-processing tool was carried out to decompose the infrared spectrum and compress the data set. The compressed data regression model was applied to simultaneous multi-component determination for coal contents. Compression performance with several wavelet functions at different resolution scales was studied, and prediction ability of the compressed regression model was investigated. Numerical experiment results show that the wavelet transform performs an effective compression preprocessing technique in multivariate calibration and enhances the ability in characteristic extraction of coal infrared spectrum. Using the compressed data regression model, the reconstructing results are almost identical compared to the original spectrum, and the original size of the data set has been reduced to about 5% while the computational time needed decreases significantly.

scholarly journals Classifying Asian Rice Cultivars (Oryza sativa L.) into Indica and Japonica Using Logistic Regression Model with Publicly Available Phenotypic Data

2018 ◽  
Author(s):  
Bongsong Kim
Keyword(s):  
Logistic Regression ◽  
Oryza Sativa ◽  
Regression Model ◽  
Prediction Accuracy ◽  
Logistic Regression Model ◽  
Rice Cultivars ◽  
Prediction Ability ◽  
Plant Seed ◽  
Asian Rice ◽  
Panicle Number

AbstractThis article introduces how to implement the logistic regression model (LRM) with phenotypic variables for classifying Asian rice (Oryza sativa L.) cultivars into two pivotal subpopulations, indica and japonica. This study took advantage of publicly available data attached to a previous paper. The classification accuracy was assessed using an area under curve (AUC) of a receiver operating characteristic (ROC) curve. Given 24 phenotypic variables for 280 indica/japonica accessions, the LRMs were fitted with up to six phenotypic variables of all possible combinations; the highest AUC accounts for 0.9977, obtained with six variables including panicle number per plant, seed number per panicle, florets per panicle, panicle fertility, straighthead susceptibility and blast resistance. Overall, the more variables there are, the higher the resulting AUCs are. The ultimate purpose of this study is to demonstrate the indica/japonica prediction ability of the LRM when applied to unclassified Asian rice cultivars. To estimate the indica/japonica prediction accuracy, ten-fold cross-validations were conducted 100 times with the 280 indica/japonica accessions using the LRM with parameters that yielded the highest AUC. The resulting prediction accuracy accounted for 0.9779. This suggests that the LRM promises to be a highly effective indica/japonica prediction tool using phenotypic variables in Asian cultivated rice.

Ease of Doing Business and Its Impact on Inward FDI

2018 ◽  
Vol 1 (1) ◽  
pp. 52 ◽  
Author(s):  
Mohamed Tareq Hossain ◽  
Zubair Hassan ◽  
Sumaiya Shafiq ◽  
Abdul Basit
Keyword(s):  
Regression Model ◽  
Causal Relationship ◽  
World Bank ◽  
Least Square ◽  
Doing Business ◽  
Least Square Regression ◽  
Foreign Countries ◽  
Ease Of Doing Business ◽  
The Impact ◽  
Starting A Business

This study investigates the impact of Ease of Doing Business on Inward FDI over the period from 2011 to 2015 across the globe. This study measures ease of doing business using starting a business, getting credit, registering property, paying taxes and enforcing contracts. The research used a sample of 177 countries from 190 countries listed in World Bank. Least square regression model via E-views software used to examine causal relationship. The study found that ease of doing business indicators ‘Enforcing Contracts’ was found to have a positive significant impact on Inward FDI. Nevertheless, ‘Getting Credit’ and ‘Registering Property’ were found to have a negative significant impact on Inward FDI. However, ‘Starting a Business’ and ‘Paying Taxes’ have no significant impact on Inward FDI in the studied timeframe of this research. The findings of the study suggested the ease of doing business enables inward FDI through better contract enforcements, getting credit and registering property. The findings of the research will assist international managers and companies to know the importance of ease of doing business when investing in foreign countries through FDI.

Dividend Payout Ratio: Persoalan Free Cashflow, Debt To Equity Ratio, Kepemilikan Institutional, Investment Opportunity, Dan Kesejahteraan Karyawan

Liquidity ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 1-11
Author(s):  
Nurlis Azhar ◽  
Helmi Chaidir
Keyword(s):  
Cash Flow ◽  
Significant Influence ◽  
Stock Exchange ◽  
Institutional Ownership ◽  
Free Cash Flow ◽  
Manufacturing Companies ◽  
Flow Ratio ◽  
Prediction Ability ◽  
Employee Welfare ◽  
Equity Ratio

This study was conducted to examine the effect of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (Parliament) partially on manufacturing companies listed on Indonesia Stock Exchange period 2011-2015. In addition, to test the feasibility of regression model, the influence of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (DPR) simultaneously at manufacturing company listed on Bursa Indonesia Securities period 2011-2015. The population in this study are 146 manufacturing companies that have been and still listed in Indonesia Stock Exchange period 2011-2013. The sampling technique used was purposive sampling and obtained sample of 42 companies. Data analysis technique used is by using multiple linear regression test. The results showed that Free Cash Flow Ratio, no significant effect on Divident Payout Ratio (DPR). Debt Equity Ratio (DER) has a negative and significant influence on Divident Payout Ratio (DPR), Institutional Ownership has a significant positive effect on Divident Payout Ratio (DPR), Employee Welfare and Price Earning Ratio (PER) has a positive and significant influence on the Divident Payout Ratio ). Simultaneously Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) give effect to Divident Payout Ratio. The prediction ability of the five variables to the Divident Payout Ratio (DPR) is 21.3% as indicated by the adjusted R square of 0.271 while the remaining 79.7% is influenced by other factors not included in the research model.

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