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中国南方渔业论坛暨第二十九次学术会议

作者： Min Tao Ping Xie Jun Chen Boqiang Qin Dawen Zhang Yuan Niu Meng Zhang Qing Wang Laiyan Wu Donghu Experimental Station of Lake Ecosystems State Key Laboratory of Freshwater Ecology and Biotec Nanjing Institute of Geography and Limnology Chinese Academy of SciencesNanjing 210008P.R.China

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of *** far, most studies on MCs have been limited to two small bays, while systematic research on the whole-lake is *** explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in *** health risks of MC exposure were high, especially in the northern *** Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis *** of powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC) , water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the *** interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation.

关键词： Microcystins MC cellular quotas generalized additive model Environmental factors Carbon fixation Health risks Lake Taihu

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Evaluation of Urban Ozone Formationby Photochemical Ozone Creation Potential Indices and generalized additive model

Evaluation of Urban Ozone Formationby Photochemical Ozone Cr...

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International Conference on Biological, Civil and Environmental Engineering (BCEE-2015, Bali)

作者： Sirithorn Saengsai Wanida Jinsart Interdisciplinary Program in Environmental and Hazardous Waste Management (International Program) Graduate SchoolChulalongkorn University Department of Environmental Science Faculty of ScienceChulalongkorn University

Ozone formation potential(OFP) of 43 VOCs species were analyzed usingthe ambient concentrations data from 2008 to 2013 and the Photochemical ozone creation potential indices,the indicator of a VOCs' capacity to contribute to the photochemical ozone formation).The ozone production potential contribution(OPC) can then be calculated as the percentage of individual OFP to the summation of all VOCs' *** predicted ozone formation potential species were verified by generalized additive model(GAM).The impacts of the photochemical ozone creation potential to surface ozone level were *** hydrocarbons were found to be the main group that affected to the ozone production40.2% with the overall mean of response and r2 changes(9.4%).Althoughfrom the calculation,the OFP level of Hydrocarbons(59.5%) and oxygenated hydrocarbons(39.4%) were high but from GAM results,their effects to ozone production(6.0% and 1.6%) were lower than those of halogenated hydrocarbons.

关键词： generalized additive model ozone formation potential photochemical ozone creation potential volatile organic compound

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Impact of air pollutants on hospital admissions and economic losses of elderly patients with cardiovascular diseases in Southwestern China: A generalized additive model

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SUSTAINABLE ENVIRONMENT 2024年第1期10卷

作者： Ji, Shuming Wu, Ni Wang, Luo Wang, Wei Zhang, Pei Qiu, Peiyuan Zhou, Xiaoyuan Sichuan Univ West China Hosp Dept Clin Res Management Chengdu Peoples R China Sichuan Univ West China Sch Publ Hlth Chengdu 610041 Peoples R China Sichuan Univ West China Hosp 4 Chengdu 610041 Peoples R China Univ Hong Kong Sch Publ Hlth Div Epidemiol & Biostat Hong Kong Peoples R China

BackgroundIn terms of hospitalization risk for the elderly, the evidences for adverse effects and economic losses of air pollutants on different types of cardiovascular diseases (CVDs) are limited. This study aims to quantify the economic losses of five major CVDs affected by air pollutants in Southwestern *** information of 358,271 participants in the top five CVDs among the elderly population was collected over a 4-year time span. The generalized additive model (GAM) was used to estimate the short-term exposure response relationship between air pollutants and elderly inpatients with CVDs. The cost of illness method (COI) was used to evaluate the corresponding direct and indirect costs, and we refer to this in the text as economic *** pollutants significantly increase the risk of hospitalisation in elderly patients with coronary heart disease (CHD), myocardial ischaemia (MI), pulmonary heart disease (PHD), hypertension, and cardiac arrhythmia. CO, NO2, PM10 and SO2 were significant increase the risk of CHD, with the RRs were 0.09, 2.59, 0.31, and 6.54, respectively;CO, NO2, O3, PM10, PM2.5, and SO2 were notably increased the risk of MI, with the RRs were 0.16, 4.19, 0.76, 0.79, 0.92, and 7.49, respectively;CO, NO2, PM10, PM2.5, and SO2 were distinctly increased the risk of PHD, with the RRs were 0.14, 2.79, 0.75, 1.00, and 3.86, respectively;NO2 and SO2 significant increase the risk of hypertension, with the RRs were 1.41 and 5.94, respectively;CO, NO2, PM10, and SO2 were distinctly increased the risk of arrhythmia, with the RRs were 0.09, 2.23, 0.36, and 5.41, respectively. Furthermore, we quantify the economic losses of five major CVDs affected by air pollutants. The economic burden of air pollution on patients with CHD, hypertension, PHD, cardiac arrhythmia, and MI were 66.38, 36.58, 5.76, 5.66 5.21 million yuan, *** study found that short-term exposure to pollutants can have a significant impact on h

关键词： Elderly population cardiovascular diseases ambient air pollution generalized additive model

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A generalized additive model for microarray gene expression data analysis

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Journal of Biopharmaceutical Statistics 2004年第3期14卷 553-573页

作者： Tsai, Chen-An Hsueh, Huey-Miin Chen, James J. Div. of Biometry and Risk Assessment Natl. Ctr. for Toxicological Res. Food and Drug Administration Jefferson AR 72079 United States Department of Statistics National Cheng-Chi University Taipei Taiwan

Microarray technology allows the measurement of expression levels of a large number of genes simultaneously. There are inherent biases in microarray data generated from an experiment. Various statistical methods have been proposed for data normalization and data analysis. This paper proposes a generalized additive model for the analysis of gene expression data. This model consists of two sub-models: a non-linear model and a linear model. We propose a two-step normalization algorithm to fit the two sub-models sequentially. The first step involves a non-parametric regression using lowess fits to adjust for non-linear systematic biases. The second step uses a linear ANOVA model to estimate the remaining effects including the interaction effect of genes and treatments, the effect of interest in a study. The proposed model is a generalization of the ANOVA model for microarray data analysis. We show correspondences between the lowess fit and the ANOVA model methods. The normalization procedure does not assume the majority of genes do not change their expression levels, and neither does it assume two channel intensities from the same spot are independent. The procedure can be applied to either one channel or two channel data from the experiments with multiple treatments or multiple nuisance factors. Two toxicogenomic experiment data sets and a simulated data set are used to contrast the proposed method with the commonly known lowess fit and ANOVA methods.

关键词： ANOVA model generalized additive model Lowess fit Microarray experiment Non-parametric regression

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Comparative performance of linear regression, polynomial regression and generalized additive model for canopy cover estimation in the dry deciduous forest of West Bengal

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2021年 22卷

作者： Bera, Dipankar Das Chatterjee, Nilanjana Bera, Sudip Vidyasagar Univ Dept Geog Midnapore 721102 W Bengal India

Canopy cover is an important biophysical indicator of forested habitats, but quantifying and monitoring with the accurate and cost-efficient method is always difficult in fragmented canopy cover areas. Therefore, this study was designed to explore the comparative performance of linear regression, polynomial regression and generalized additive model (GAM), and assess the important predictors for canopy cover estimation in the dry deciduous forest of West Bengal. We used the Sentinel-2 based individual bands and spectral indices as predictor variables from the different composition of imagery in the growing season (June-September) between 2018 and 2020. Canopy cover of 667 plots were measured using collect earth online (CEO) from high resolution imagery. Univariate and multiple models were fitted and assessed by 10 fold cross-validation. Root mean square error (RMSE) and the coefficient of determination (R-2) was used for the evaluation of model performance. The result showed that univariate models using GAM were fitted better followed polynomial regression and linear regression model. SWIR bands and SWIR bands based indices were fitted well than other groups of variables. Individual bands based multiple prediction models performed better followed by NIR, SWIR, atmospheric and red edge indices based multiple prediction models for multiple linear regression, multiple polynomial regression and multiple GAM. Multiple GAM performed better (RMSE = 0.163, R-2 = 0.777) than multiple polynomial regression (RMSE = 0.167, R-2 = 0.772) and multiple linear regression model (RMSE = 0.182, R-2 = 0.721). Although, the difference of performance between multiple polynomial regression model and multiple GAM was negligible. But in respect to prediction range, and overestimation and underestimation multiple GAM was superior to multiple polynomial regression model. The study demonstrates that the potential utility of GAM for canopy cover estimation and GAM can be superior to linear and po

关键词： Sentinel-2 Google earth engine Multiple prediction model generalized additive model Canopy cover Vegetation indices

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An integrated Data Envelopment Analysis and generalized additive model for assessing managerial ability with application to the insurance industry

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Decision Analytics Journal 2022年 4卷

作者： Banker, Rajiv. D. Amirteimoori, Alireza Sinha, Ram Pratap Merves Chair in Accounting and Information Technology Fox School of Business Temple University Philadelphia United States Department of Applied Mathematics and Operations Research Rasht branch Islamic Azad University Rasht Iran Government College of Engineering & Leather Technology West Bengal Kolkata 700106 India

Managerial ability plays a vital role in the performance of business enterprises;however, such efforts are not directly observable. This study estimates managerial ability within the insurance sector of the economy based on two data sets from two different Asian countries. We estimate insurer-wise efficiency using Data Envelopment Analysis (DEA) and regress the logarithm of the obtained technical efficiency on a set of contextual variables influencing insurer performance. In the next stage, we estimate managerial ability from regression residuals (the difference between the observed and fitted values of insurer efficiency). Furthermore, we use this measure of managerial ability as an independent variable for explaining return on equity based on ordinary least squares and general additive model (GAM) regression and show the relationship is highly significant. We compare and contrast the average technical efficiency and managerial ability in the two Asian countries. © 2022 The Author(s)

关键词： Data Envelopment Analysis Efficiency generalized additive model Managerial ability Performance measurement Prescriptive analytics Regression

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generalized Sparse additive model with Unknown Link Function 24

Generalized Sparse Additive Model with Unknown Link Function

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Yuan, Peipei You, Xinge Chen, Hong Zhang, Xuelin Peng, Qinmu School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798331506681

generalized additive models (GAMs) have been successfully applied to high dimensional data. However, most existing methods cannot capture the high level feature patterns from complex data. To alleviate this problem, we propose a new sparse additive model, named generalized sparse additive model with unknown link function (GSAMUL), in which the component functions are estimated by B-spline basis and the unknown link function is estimated by a multi-layer perceptron (MLP) network. Furthermore, l2.1 -norm regularizer is used for variable selection. The proposed GSAMUL can realize both variable selection and hidden interaction. We integrate this estimation into a bilevel optimization problem, where the data is split into training set and validation set. In theory, we provide the guarantees about the convergence of the approximate procedure. In applications, experimental evaluations on both synthetic and real world data sets consistently validate the effectiveness of GSAMUL. © 2024 IEEE.

关键词： bilevel optimization convergence analysis generalized additive model unknown link function variable interaction

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Integration of generalized additive models and hyperspectral imaging for quality prediction in Chaoshan beef meatballs: A comprehensive "raw materials-processing-quality-sensory" model

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FOOD RESEARCH INTERNATIONAL 2025年 204卷 115961页

作者： He, Yongzhe You, Qian Tang, Shuqi Mao, Runxiang Yuan, Yukun Tian, Xingguo Xu, Xiaoyan South China Agr Univ Coll Food Sci Guangdong Prov Key Lab Food Qual & Safety Guangzhou 510642 Peoples R China South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

Maintaining consistent quality of Chaoshan beef meatballs (CBMs) is challenging due to the intricate production processes and stringent raw material requirements, with no effective method currently available for real-time monitoring during production. In this study, a comprehensive "raw materials-processing-quality-sensory" network model to predict CBMs quality attributes by integrating generalized additive model (GAM) with (HSI) was developed. GAM showed strong predictive performance for key attributes such as the pH (R2 = 0.837), saltsoluble protein content (R2 = 0.909) of the beef batter, and the proportion of free water in CBMs (R2 = 0.850). However, the model was less accurate for predicting for the hardness of CBMs (R2 = 0.781) and total sensory score (R2 = 0.772). To address this, HSI technology was introduced to enhance the generalization capability of GAM. The optimal models using HSI for predicting pH and salt-soluble protein content of beef batter were SNVCARS-GLGCM-BP-ANN (Rp2 = 0.959, RMSEp = 0.035) and SNV-CARS-GLGCM-PLSR (Rp2 = 0.983, RMSEp = 0.101), respectively. Incorporating these predictions into the GAM improved predictive accuracy for CBMs hardness and total sensory score, increasing from 78.1 % and 77.2 % to 83.0 % each. This work provides a theoretical basis for optimizing CBMs processing and real-time food quality monitoring.

关键词： Chaoshan beef meatballs generalized additive model Hyperspectral imaging Process optimization Food quality

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modeling Nonlinear Structural Equation models: A Comparison of the Two-Stage generalized additive models and the Finite Mixture Structural Equation model

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STRUCTURAL EQUATION modelING-A MULTIDISCIPLINARY JOURNAL 2015年第1期22卷 60-75页

作者： Finch, W. Holmes Ball State Univ Muncie IN 47304 USA

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.

关键词： finite mixture SEM generalized additive model nonlinear SEM

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Probabilistic Precipitation Forecasting Based on Ensemble Output Using generalized additive models and Bayesian model Averaging

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Acta meteorologica Sinica 2012年第1期26卷 1-12页

作者：杨赤严中伟邵月红 College of Global Change and Earth System Science Beijing Normal University Key Laboratory of Regional Climate-Environment Research for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

关键词： Bayesian model averaging generalized additive model probabilistic precipitation forecasting,TIGGE Tweedie distribution

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