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Fitting Log-Gaussian Cox Processes Using generalized additive model Software

AMERICAN STATISTICIAN 2024年第4期78卷 418-425页

作者： Dovers, Elliot Stoklosa, Jakub Warton, David I. UNSW Sydney Sch Math & Stat Sydney NSW Australia UNSW Sydney Evolut & Ecol Res Ctr Sydney NSW Australia

While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model software, via a simple line of code, demonstrated on R by the popular mgcv package. We are able to do this because a common and computationally efficient way to fit a log-Gaussian Cox process model is to use a basis function expansion to approximate the Gaussian random field, as is provided by a generic bivariate smoother over geographic space. We further show that if basis functions are parameterized appropriately then we can estimate parameters in the spatial covariance function for the latent random field using a generalized additive model. We use simulation to show that this approach leads to model fits of comparable quality to state-of-the-art software, often more quickly. But we see the main advance from this work as lowering the technology barrier to spatial statistics for applied researchers, many of whom are already familiar with generalized additive model software.

关键词： Basis functions generalized additive model Point processes Spatial statistics

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Adaptive Load Forecasting Methodology Based on generalized additive model with Automatic Variable Selection

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SENSORS 2022年第19期22卷 7247-7247页

作者： Krstonijevic, Sovjetka Univ Belgrade Inst Mihajlo Pupin Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology.

关键词： electricity load forecasting generalized additive model automatic variable selection smart grid

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Climate driven trends in London's urban heat island intensity reconstructed over 70 years using a generalized additive model

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URBAN CLIMATE 2021年 40卷

作者： Bassett, R. Janes-Bassett, V. Phillipson, J. Young, P. J. Blair, G. S. Univ Lancaster Lancaster Environm Ctr Lancaster England Univ Lancaster Sch Comp & Commun Lancaster England Univ Lancaster Ctr Excellence Environm Data Sci A Joint Ctr UK Ctr Ecol Hydrol UKCEH Lancaster England

Long-term urban heat island (UHI) observations are uncommon and where available, are generally unable to distinguish changing climate drivers from urban expansion;neither driver is treated independently. We overcome this limitation using a generalized additive model to learn the variability in UHI intensity (UHII) at a central London weather station (St James's Park) over a 10-year observation period (2010-2019). We then use the model to reconstruct 70 years (1950-2019) of monthly night-time UHII variability using ERA5 reanalysis data both as a reference in UHII calculation and for the predictors. We find considerable variability both seasonally and annually within the UHII time series (monthly mean maximum UHIIs are 1.4-2.9 degrees C). Applying extreme value analysis to the time series we show that monthly mean maximum UHIIs are likely to exceed 2.75 degrees C once every 11 years. Considering that most studies observe or model UHIIs for less than a year, they will likely misrepresent this UHII variability. Nevertheless, despite moving to a warmer background climate, London's UHII has not significantly changed across the period of analysis (1950-2019). The data-driven methods we create in this study are easily transferable to other cities.

关键词： Climate generalized additive model GAM Extreme values Time series Urban heat island UHI Variability

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Use of a generalized additive model for a spatial analysis of bovine brucellosis risk in the state of Mato Grosso in 2002 and 2014

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PREVENTIVE VETERINARY MEDICINE 2020年第0期176卷 104938-000页

作者： Silva, Isana Souza Ioris Barddal, Janice Elena Negreiros, Risia Lopes Oliveira, A. C. S. Aguiar, D. M. Fed Univ Mato Grosso FAVET UFMT Fac Vet Med Postgrad Programme Vet Sci 2367 Fernando Correa Ave BR-78060900 Cuiaba MT Brazil Fed Superintendence Agr State Mato Grosso Cuiaba MT Brazil Agr Def Inst Mato Grosso State Div Epidemiol INDEA MT 1000 Arquimedes Pereira Lima Ave BR-78068635 Cuiaba MT Brazil Univ Fed Mato Grosso ICT UFMT Dept Stat Inst Exact Sci & Land 2367 Fernando Correa Ave BR-78060900 Cuiaba MT Brazil Univ Fed Mato Grosso HOVET FAVET Lab Virol & Rickettsiosis 2367 Fernando Correa Ave BR-78060900 Cuiaba MT Brazil

Diseases that affect cattle represent obstacles to the development of livestock activity. Brucellosis is a significant such disease because it is transmissible, has a chronic nature, and causes health and economic damages to the herd and rural producer. Data from surveys performed in 2002 and 2014 were compared to identify the spatial distribution of bovine brucellosis and to evaluate clusters of outbreaks and areas of greater risk to have infected cattle in the state of Mato Grosso, Brazil. The present study analyzed the data obtained in the aforementioned investigations with a statistical model based on a spatial point process called a generalized additive model (GAM). The analysis made it possible to identify the regions of highest and lowest risk in the state of Mato Grosso. Of the 1001 properties analyzed in 2002, 198 were in areas with high-odds ratio, and 121 were in a low-odds ratio area. Of the 1248 properties sampled in 2014, 119 were in a high-odds ratio area, and 162 were in a low-odds ratio area. Areas with high-odds ratio are more likely to have infected cattle and can be considered to be at higher risk for the disease. The results of the present study highlight the reduction in foci, prevalence, and its relationship with the spatial distribution of bovine brucellosis. The study results should help the official defense service of Mato Grosso direct its activities according to the profile of each region.

关键词： Brucella abortus Outbreak generalized additive model Brazil

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Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada

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SPATIAL STATISTICS 2022年第0期49卷 100526页

作者： Feng, Cindy Dalhousie Univ Fac Med Dept Community Hlth & Epidemiol Halifax NS B3H 1V7 Canada

This article presents a spatial-temporal generalized additive model for modeling geo-referenced COVID-19 mortality data in Toronto, Canada. A range of factors and spatial-temporal terms are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and average of income, are modeled as a two-dimensional spline smoother. The change of spatial pattern over time is modeled as a three-dimensional tensor product smoother. By fitting this model, the space-time effect can uncover the underlying spatial-temporal pattern that is not explained by the covariates. The performance of the modeling method based on the individual data is also compared to the modeling methods based on the aggregated data in terms of in-sample and out-of-sample predictive checking. The results suggest that the individual-level based analysis provided a better overall model fit and higher predictive accuracy for detecting epidemic peaks in this application as compared to the analysis based on the aggregated data. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 mortality generalized additive model Space-time interaction Thin plate spline Tensor product smooth

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High-dimensional sparse predictive modeling applied to varied correlation structure via the generalized additive model

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Procedia Computer Science 2024年 234卷 70-78页

作者： Farlley G. Bondaug Bernadette F. Tubo Manticao Misamis Oriental 9024 Philippines Department of Mathematics and Statistics MSU-Iligan Institute of Technology 9200 Iligan City Philippines

This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the generalized additive model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not affected by the increase of feature dimension.

关键词： principal components analysis orthogonal PCA super sparse PCA generalized additive model

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Use of a generalized additive model to Examine Factors Influencing the CPUE of Uroteuthis edulis

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台湾水产学会刊 2015年第2期42卷 73-85页

作者：王凱毅陳均龍廖正信吳繼倫張可揚

The swordtip squid, Uroteuthis edulis, is an important fishery resource in the southern East China Sea since 1950s. In this study, a generalized additive model (GAM) was applied to investigate the influence factors on the U. edulis catches, including temporal factors (year, month, and lunar phase), spatial factors (longitude, latitude, and depth), fishing efficiency (tonnage, engine power, and light intensity) and environmental factors (sea surface temperature and chlorophyll-a) on catch per unit effort (CPUE) of the swordtip squid catches by torch-light fisheries (2009-2014). The results indicated that the temporal, spatial and fishing efficiency were significant explanatory factors (36.7% of variance explained) of CPUE. Relative higher CPUE was shown in April and September of the research period, which represented the starting (feeding group) and ending (spawning group) of the fishing seasons respectively. In addition, the highest CPUE was detected in areas higher than 26°N, where the Kuroshio Current and China Coastal Current are met and forming front, suitable for living of the U. edulis. The positive relationship between fishing efficiency and CPUE were determined. Due to the U. edulis has various habitats (e.g. upwelling and front areas), thus significantly effects of environmental factors on CPUE are not statistical significant. In future research, it is necessary to divide the fishing grounds into several specific areas for further determining the relationships between the environmental factors and CPUE of U. edulis.

关键词： East China Sea Uroteuthis edulis Catch generalized additive model

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Atmospheric pollen concentrations and chronic obstructive pulmonary disease (COPD) patients visits in Beijing: time series analysis using a generalized additive model

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SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Liu, Aizhu Sheng, Weixuan Tang, Xianshi Capital Med Univ Affiliated Beijing Shijitan Hosp Dept Otolaryngol Head & Neck Surg Beijing 100038 Peoples R China Capital Med Univ Affiliated Beijing Shijitan Hosp Dept Anesthesiol Beijing 100038 Peoples R China Jiangsu Inst Parasit Dis Key Lab Natl Hlth Commiss Parasit Dis Control & Pr Key Lab Jiangsu Prov Parasite & Vector Control Tec Wuxi 214064 Peoples R China

To investigate the correlation between the daily visits of chronic obstructive pulmonary disease (COPD) patients in hospital clinic and pollen concentrations in Beijing. We collected daily visits of COPD patients of Beijing Shijitan Hospital from April 1st, 2019 to September 30th, 2019. The relationship between pollen concentrations and COPD patient number was analyzed with meteorological factors, time trend, day of the week effect and holiday effect being controlled by the generalized additive model of time series analysis. R4.1.2 software was applied to generate Spearman correlation coefficient, specific and incremental cumulative effect curves of relative risks as well as the response and three-dimensional diagrams for the exposure lag effect prediction. The fitting models were used to predict the lag relative risk and 95% confidence intervals for specific and incremental cumulative effects of specific pollen concentrations. The number of COPD patients was positively correlated with pollen concentration. When pollen concentration increased by 10 grains/1000 mm2, the peak value of the specific cumulative effect appeared on day0, with the effect gone on day4 and a lag time of 4 days observed, whereas the incremental cumulative effect's peak value was shown on day17, and the effect disappeared on day18, with a lag time of 18 days. The results showed that pollen concentration was not only positively correlated with the number of COPD patients, but also had a bimodal lag effect on COPD visits in the hospital at Beijing.

关键词： Chronic obstructive pulmonary disease Environmental meteorological factors Pollen generalized additive model Time series analysis

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Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM)

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HELIYON 2024年第4期10卷

作者： Amin, Ruhul Salan, Md Sifat Ar Hossain, Md Moyazzem Jahangirnagar Univ Dept Stat & Data Sci Dhaka 1342 Bangladesh

The indicators of economic and sustainable development ultimately significantly depend on carbon dioxide (CO2) emissions in every country. In Bangladesh, there is an increasing trend in population, industrialization, as well as electricity demand generated from different sources, ultimately increasing CO2 emissions. This study explores the relationship between CO2 emissions and other significant relevant indicators. Moreover, the authors aimed to identify which model is effective at predicting CO2 emissions and assess the accuracy of the prediction of different models. The secondary data from 1971 to 2020, was collected from the World Bank and the Bangladesh Road Transport Authority's publicly accessible website. The generalized additive model (GAM), the polynomial regression (PR), and multiple linear regression (MLR) were used for modeling CO2 emissions. The model performance is evaluated using the Bayesian information criterion (BIC), Akaike information criterion (AIC), Root mean square error (RMSE), R-square, and mean square error (MSE). Results revealed that there are few multicollinearity problems in the datasets and exhibit a nonlinear relationship among CO2 emissions. Among the models considered in this study, the GAM model has the lowest value of RMSE = 0.008, MSE = 0.000063, AIC = -303.21, BIC = -266.64 and the highest value of R-squared = 0.996 compared to the MLR and PR models, suggesting the most appropriate model in predicting CO2 emissions in Bangladesh. Findings revealed that the total CO2 emissions and other relevant risk factors is non-linear. The study suggests that the generalized additive model regression technique can be used as an effective tool for predicting CO2 emissions in Bangladesh. The authors believed that the findings would be helpful to policymakers in designing effective strategies in the areas of a low-carbon economy, encouraging the use of renewable energy sources, and focusing on technological advancement that reduces CO2 emission

关键词： CO 2 emissions Nonparametric model generalized additive model Bangladesh

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A generalized additive model (GAM) approach to principal component analysis of geographic data

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SPATIAL STATISTICS 2024年 59卷

作者： Lopez, Francisco de Asis Ordonez, Celestino Roca-Pardinas, Javier Univ Vigo Dept Stat & Operat Res Vigo 36310 Spain Univ Oviedo Dept Min Exploitat & Prospecting Mieres 33600 Spain

Geographically Weighted Principal Component Analysis (GWPCA) is an extension of classical PCA to deal with the spatial heterogeneity of geographical data. This heterogeneity results in a variance-covariance matrix that is not stationary but changes with the geographical location. Despite its usefulness, this method presents some unsolved issues, such as finding an appropriate bandwidth (size of the vicinity) as a function of the retained components. In this work, we address the problem of calculating principal components for geographical data from a new perspective that overcomes this problem. Specifically we propose a scale-location model which uses generalized additive models (GAMs) to calculate means for each variable and a correlation matrix that relates the variables, both depending on the spatial location. It should be noticed that although we deal with geographic data, our methodology cannot be considered strictly spatial since we assume that there is not a spatial correlation structure in the error term. Our approach does not require to calculate an optimal bandwidth as a function of the number of components retained in the analysis. Instead, the covariance matrix is estimated using smooth functions adapted to the data, so the smoothness can be different for each element of the matrix. The proposed methodology was tested with simulated data and compared with GWPCA. The result was a better representation of the data structure in the proposed method. Finally, we show the possibilities of our method in a problem with real data regarding air pollution and socioeconomic factors.

关键词： generalized additive model Principal componnents analysis Location model Geographical data

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