scholarly journals Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China

2021 ◽  
Vol 13 (16) ◽  
pp. 3123
Author(s):  
Chunzhu Wei ◽  
Qianying Zhao ◽  
Yang Lu ◽  
Dongjie Fu
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Regression Model ◽  
Pearl River Delta ◽  
Regression Models ◽  
River Delta ◽  
Pearl River ◽  
Linear Regression Models ◽  
Random Forest Regression ◽  
Band Combinations

Pearl River Delta (PRD), as one of the most densely populated regions in the world, is facing both natural changes (e.g., sea level rise) and human-induced changes (e.g., dredging for navigation and land reclamation). Bathymetric information is thus important for the protection and management of the estuarine environment, but little effort has been made to comprehensively evaluate the performance of different methods and datasets. In this study, two linear regression models—the linear band model and the log-transformed band ratio model, and two non-linear regression models—the support vector regression model and the random forest regression model—were applied to Landsat 8 (L8) and Sentinel-2 (S2) imagery for bathymetry mapping in 2019 and 2020. Results suggested that a priori area clustering based on spectral features using the K-means algorithm improved estimation accuracy. The random forest regression model performed best, and the three-band combinations outperformed two-band combinations in all models. When the non-linear models were applied with three-band combination (red, green, blue) to L8 and S2 imagery, the Root Mean Square Error (Mean Absolute Error) decreased by 23.10% (35.53%), and the coefficient of determination (Kling-Gupta efficiency) increased by 0.08 (0.09) on average, compared to those using the linear regression models. Despite the differences in spatial resolution and band wavelength, L8 and S2 performed similarly in bathymetry estimation. This study quantified the relative performance of different models and may shed light on the potential combination of multiple data sources for more timely and accurate bathymetry mapping.

scholarly journals Applying Random Forest Model Algorithm to GFR Estimation

2020 ◽  
Author(s):  
Peijia Liu ◽  
Dong Yang ◽  
Shaomin Li ◽  
Yutian Chong ◽  
Wentao Hu ◽  
...  
Keyword(s):  
Random Forest ◽  
Kidney Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Random Forest Regression ◽  
Variable Model ◽  
Data Set ◽  
Development Data ◽  
Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equationMethods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy , precision and root mean square error(RMSE).Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P<0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P<0.01, 19.08 vs 20.60, P<0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P=0.10, 0.8 vs 0.78, P=0.19, respectively).Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

scholarly journals Applying Random Forest Model Algorithm to GFR estimation

2020 ◽  
Author(s):  
Peijia Liu ◽  
Dong Yang ◽  
Shaomin Li ◽  
Yutian Chong ◽  
Ming Li ◽  
...  
Keyword(s):  
Random Forest ◽  
Kidney Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Random Forest Regression ◽  
Variable Model ◽  
Data Set ◽  
Development Data ◽  
Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P < 0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P < 0.01, 19.08 vs 20.60, P < 0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

Estimation in Dynamic Linear Regression Models with Infinite Variance Errors

1993 ◽  
Vol 9 (4) ◽  
pp. 570-588 ◽  
Author(s):  
Keith Knight
Keyword(s):  
Asymptotic Behavior ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Infinite Variance ◽  
Linear Regression Models ◽  
Asymptotically Normal ◽  
Second Moments ◽  
True Values

This paper considers the asymptotic behavior of M-estimates in a dynamic linear regression model where the errors have infinite second moments but the exogenous regressors satisfy the standard assumptions. It is shown that under certain conditions, the estimates of the parameters corresponding to the exogenous regressors are asymptotically normal and converge to the true values at the standard n−½ rate.

scholarly journals Brazilian Articles in Top-Tier Dental Journals and Influence of International Collaboration on Citation Rates

2019 ◽  
Vol 30 (4) ◽  
pp. 307-316 ◽  
Author(s):  
Ana Paula R Gonçalves ◽  
Bruna L Porto ◽  
Bruna Rodolfo ◽  
Clovis M Faggion Jr ◽  
Bernardo A. Agostini ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
International Collaboration ◽  
Linear Regression Model ◽  
Regression Models ◽  
Dental Materials ◽  
Original Research ◽  
Linear Regression Models ◽  
Article Type ◽  
Multilevel Linear Regression

Abstract This study investigated the presence of co-authorship from Brazil in articles published in top-tier dental journals and analyzed the influence of international collaboration, article type (original research or review), and funding on citation rates. Articles published between 2015 and 2017 in 38 selected journals from 14 dental subareas were screened in Scopus. Bibliographic information, citation counts, and funding details were recorded for all articles (N=15619). Collaboration with other top-10 publishing countries in dentistry was registered. Annual citations averages (ACA) were calculated. A linear regression model assessed differences in ACA between subareas. Multilevel linear regression models evaluated the influence of article type, funding, and presence of international collaboration in ACA. Brazil was a frequent co-author of articles published in the period (top 3: USA=25.5%; Brazil=13.8%; Germany=9.2%) and the country with most publications in two subareas. The subjects with the biggest share of Brazil are Operative Dentistry/Cariology, Dental Materials, and Endodontics. Brazil was second in total citations, but fifth in citation averages per article. From the total of 2155 articles co-authored by Brazil, 74.8% had no co-authorship from other top-10 publishing countries. USA (17.8%), Italy (4.2%), and UK (3.2%) were the main co-author countries, but the main collaboration country varied between subjects. Implantology and Dental Materials were the subjects with most international co-authorship. Review articles and articles with international collaboration were associated with increased citation rates, whereas the presence of study funding did not influence the citations.

scholarly journals Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 525
Author(s):  
Samer A Kharroubi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Random Effects ◽  
Regression Models ◽  
Mean Squared Error ◽  
Health State Utility ◽  
Beta Regression ◽  
Health State ◽  
Linear Regression Models ◽  
Utility Scores

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (−1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.

scholarly journals On the least trimmed squares estimators for JS circular regression model

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Shokrya Saleh Alshqaq ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Real Data ◽  
Linear Regression Models ◽  
Least Trimmed Squares ◽  
Leverage Points ◽  
Circular Regression ◽  
Robust Linear Regression

The least trimmed squares (LTS) estimation has been successfully used in the robust linear regression models. This article extends the LTS estimation to the Jammalamadaka and Sarma (JS) circular regression model. The robustness of the proposed estimator is studied and the used algorithm for computation is discussed. Simulation studied, and real data show that the proposed robust circular estimator effectively fits JS circular models in the presence of vertical outliers and leverage points.

scholarly journals PERILAKU KECURANGAN AKADEMIK MAHASISWA AKUNTANSI : DIMENSI FRAUD PENTAGON (STUDI KASUS PADA MAHASISWA PRODI AKUNTANSI UKRIDA)

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Kennedy Fadersair ◽  
Subagyo Subagyo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Sampling Method ◽  
Accounting Students ◽  
Linear Regression Models ◽  
Purposive Sampling ◽  
Academic Fraud

<p class="Default"><strong><em>ABSTRACT:</em></strong><em> This research examined factors that influence the behaviour of student’s cheating by using the concept of fraud pentagon consisting of pressure, opportunity, rationalization, competence and arrogance</em>. <em>In collecting data using questionnaires with purposive sampling method. The regression model used in this study is the linear regression models with SPSS 24. Participants in this study were 122 accounting students in Faculty of Economics and Business Christian Krida Wacana University. The result of this research shows that simultaneously fraud pentagon have significant effect to student’s academic fraud behavior. Partially, pressure and competence have positive significant effect to student’s academic fraud behavior. Arrogance have negative significant effect to student’s academic fraud behavior. Rationalization and opportunity did not influence.</em></p><p><strong><em> </em></strong></p><p><strong><em>Keyword</em></strong><em> : </em><em>academic fraud, pressure, opportunity, rasionalization, capability</em><em>,arrogance</em><em>.</em></p><p><em> </em></p><p class="Default"><strong>ABSTRAK:</strong> Penelitian ini untuk menguji faktor-faktor yang berpengaruh terhadap perilaku kecurangan akademik mahasiswa menggunakan konsep <em>fraud pentagon</em>, yaitu tekanan, kesempatan, rasionalisasi, kemampuan dan arogansi. Dalam pengumpulan data menggunakan kuesioner dengan metode <em>purposive sampling</em>. Model regresi yang digunakan dalam penelitian ini adalah model regresi linear berganda dengan bantuan SPSS 24. Sampel Penelitian sebanyak 122 mahasiswa Program Studi Akuntansi Fakultas Ekonomi dan Bisnis Universitas Kristen Krida Wacana. Hasil penelitian ini secara simultan menunjukkan bahwa <em>fraud pentagon</em> beperngaruh secara signifikan. Secara parsial menunjukkan bahwa tekanan dan kemampuan berpengaruh signifikan positif terhadap perilaku kecurangan akademik. Arogansi berpengaruh signifikan negatif terhadap perilaku kecurangan akademik. Rasionalisasi dan kesempatan tidak berpengaruh signifikan.</p><p> </p><p class="Default"><strong>Kata kunci : </strong>kecurangan akademik, tekanan, kesempatan, rasionalisasi, kemampuan,</p><p class="Default">                   arogansi.</p>

scholarly journals Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions

2009 ◽  
Vol 6 (1) ◽  
pp. 115-141 ◽  
Author(s):  
P. C. Stolk ◽  
C. M. J. Jacobs ◽  
E. J. Moors ◽  
A. Hensen ◽  
G. L. Velthof ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Surface Fluxes ◽  
Linear Regression Models ◽  
Open Questions ◽  
Non Linear

Abstract. Chambers are widely used to measure surface fluxes of nitrous oxide (N2O). Usually linear regression is used to calculate the fluxes from the chamber data. Non-linearity in the chamber data can result in an underestimation of the flux. Non-linear regression models are available for these data, but are not commonly used. In this study we compared the fit of linear and non-linear regression models to determine significant non-linearity in the chamber data. We assessed the influence of this significant non-linearity on the annual fluxes. For a two year dataset from an automatic chamber we calculated the fluxes with linear and non-linear regression methods. Based on the fit of the methods 32% of the data was defined significant non-linear. Significant non-linearity was not recognized by the goodness of fit of the linear regression alone. Using non-linear regression for these data and linear regression for the rest, increases the annual flux with 21% to 53% compared to the flux determined from linear regression alone. We suggest that differences this large are due to leakage through the soil. Macropores or a coarse textured soil can add to fast leakage from the chamber. Yet, also for chambers without leakage non-linearity in the chamber data is unavoidable, due to feedback from the increasing concentration in the chamber. To prevent a possibly small, but systematic underestimation of the flux, we recommend comparing the fit of a linear regression model with a non-linear regression model. The non-linear regression model should be used if the fit is significantly better. Open questions are how macropores affect chamber measurements and how optimization of chamber design can prevent this.

scholarly journals PERILAKU KECURANGAN AKADEMIK MAHASISWA AKUNTANSI : DIMENSI FRAUD PENTAGON (STUDI KASUS PADA MAHASISWA PRODI AKUNTANSI UKRIDA)

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Kennedy Fadersair ◽  
Subagyo Subagyo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Models ◽  
Sampling Method ◽  
Accounting Students ◽  
Linear Regression Models ◽  
Purposive Sampling ◽  
Academic Fraud

<p class="Default"><strong><em>ABSTRACT:</em></strong><em> This research examined factors that influence the behaviour of student’s cheating by using the concept of fraud pentagon consisting of pressure, opportunity, rationalization, competence and arrogance</em>. <em>In collecting data using questionnaires with purposive sampling method. The regression model used in this study is the linear regression models with SPSS 24. Participants in this study were 122 accounting students in Faculty of Economics and Business Christian Krida Wacana University. The result of this research shows that simultaneously fraud pentagon have significant effect to student’s academic fraud behavior. Partially, pressure and competence have positive significant effect to student’s academic fraud behavior. Arrogance have negative significant effect to student’s academic fraud behavior. Rationalization and opportunity did not influence.</em></p><p><strong><em> </em></strong></p><p><strong><em>Keyword</em></strong><em> : </em><em>academic fraud, pressure, opportunity, rasionalization, capability</em><em>,arrogance</em><em>.</em></p><p><em> </em></p><p class="Default"><strong>ABSTRAK:</strong> Penelitian ini untuk menguji faktor-faktor yang berpengaruh terhadap perilaku kecurangan akademik mahasiswa menggunakan konsep <em>fraud pentagon</em>, yaitu tekanan, kesempatan, rasionalisasi, kemampuan dan arogansi. Dalam pengumpulan data menggunakan kuesioner dengan metode <em>purposive sampling</em>. Model regresi yang digunakan dalam penelitian ini adalah model regresi linear berganda dengan bantuan SPSS 24. Sampel Penelitian sebanyak 122 mahasiswa Program Studi Akuntansi Fakultas Ekonomi dan Bisnis Universitas Kristen Krida Wacana. Hasil penelitian ini secara simultan menunjukkan bahwa <em>fraud pentagon</em> beperngaruh secara signifikan. Secara parsial menunjukkan bahwa tekanan dan kemampuan berpengaruh signifikan positif terhadap perilaku kecurangan akademik. Arogansi berpengaruh signifikan negatif terhadap perilaku kecurangan akademik. Rasionalisasi dan kesempatan tidak berpengaruh signifikan.</p><p> </p><p class="Default"><strong>Kata kunci : </strong>kecurangan akademik, tekanan, kesempatan, rasionalisasi, kemampuan,</p><p class="Default">                   arogansi.</p>

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