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A sparse estimate based on variational approximations for semiparametric generalized additive models

COMPUTATIONAL STATISTICS 2024年第4期39卷 1971-1992页

作者： Yang, Fan Yang, Yuehan Cent Univ Finance & Econ Sch Stat & Math Beijing Peoples R China

In semiparametric regression, traditional methods such as mixed generalized additive models (GAM), computed via Laplace approximation or variational approximation using penalized marginal likelihood estimation, may not achieve sparsity and unbiasedness simultaneously, and may sometimes suffer from convergence problems. To address these issues, we propose an estimator for semiparametric generalized additive models based on the marginal likelihood. Our approach provides sparsity estimates and allows for statistical inference. To estimate and select variables, we use the smoothly clipped absolute deviation penalty (SCAD) within the framework of variational approximation. We also propose efficient iterative algorithms to obtain estimations. Simulation results support our theoretical characteristics, and we demonstrate that our method is more effective than the original variational approximations framework and many other penalized methods under certain conditions. Moreover, applications with actual data further demonstrate the superior performance of the proposed method.

关键词： Variational approximation generalized additive models Penalized splines Semiparametric regression

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generalized additive models to assess Truck Driver's Perceived LOS of Signalized Intersections

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Transportation Research Procedia 2025年 82卷 2232-2244页

作者： Sujeet Sahoo Changati Sudha Prasanta kumar Bhuyan Ph. D. Scholar Civil Engineering department National Insititute of Technology Rourkela Odisha 769008 India Former M. Tech. Student National Insititute of Technology Rourkela Odisha 769008 India Asisstant professor National Insititute of Technology Rourkela Odisha 769008 India

Modern freight transportation relies on trucks delivering millions of packages daily. There is limited research on urban infrastructure service quality, particularly signalized intersections for truck traffic use. Trucks experiences different service levels due to their low maneuverability, acceleration and deceleration. As India's economy grows rapidly in turn, resulting high truck demand in turn requiring truck service quality examination. This study collects traffic, geometric and conglomerative data from five Indian cities with 30 signalized intersections and 105 approaches. Truck drivers' satisfaction is collected at city outskirts where truck traffic is significant. Truck Level of Service (TLOS) is modelled using variables which have statistically significant correlation with perceived satisfaction score using Spearman correlation analysis. generalized additive models (GAMs) use these variables as inputs. It is a intermediate modelling approach to black box machine learning algorithms and simple statistical models; which has a strong capability of handling the non-linearity in the perceived scores. In this study, six parameters affect TLOS, while some manuals use V/C ratio or control delay only for LOS estimation. Final GAM-TLOS model shows high accuracy with 0.92 R2. GAM-TLOS scores for service classes A-F obtained by linear interpretation of Likert Scale is valuable tool for Planners and designers.

关键词： Truck Traffic Heterogeinous Traffic Level of Service Perception Modelling generalized additive models

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Distill knowledge of additive tree models into generalized linear models: a new learning approach for non-smooth generalized additive models

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ANNALS OF ACTUARIAL SCIENCE 2024年第3期18卷 692-711页

作者： Maillart, Arthur Robert, Christian Detralytics Saint Josse Ten Noode Belgium Univ Lyon 1 Inst Sci Financiere & Assurances Lyon France Ctr Res Econ & Stat Lab Finance & Insurance LFA CREST Paris France

generalized additive models (GAMs) are a leading model class for interpretable machine learning. GAMs were originally defined with smooth shape functions of the predictor variables and trained using smoothing splines. Recently, tree-based GAMs where shape functions are gradient-boosted ensembles of bagged trees were proposed, leaving the door open for the estimation of a broader class of shape functions (e.g. Explainable Boosting Machine (EBM)). In this paper, we introduce a competing three-step GAM learning approach where we combine (i) the knowledge of the way to split the covariates space brought by an additive tree model (ATM), (ii) an ensemble of predictive linear scores derived from generalized linear models (GLMs) using a binning strategy based on the ATM, and (iii) a final GLM to have a prediction model that ensures auto-calibration. Numerical experiments illustrate the competitive performances of our approach on several datasets compared to GAM with splines, EBM, or GLM with binarsity penalization. A case study in trade credit insurance is also provided.

关键词： additive tree ensembles auto-calibration generalized additive models generalized linear models partitioning methods XAI

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Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using generalized additive models

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ATMOSPHERE 2022年第1期13卷 24页

作者： Hou, Kun Xu, Xia Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China Jiangsu Prov Hydrol & Water Resources Invest Bur Nanjing 210029 Peoples R China

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, -23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O-3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.

关键词： meteorological factors air pollutants marginal effect generalized additive models

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Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis

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STATISTICS IN MEDICINE 2022年第20期41卷 3899-3914页

作者： Guo, Boyi Jaeger, Byron C. Rahman, A. K. M. Fazlur Long, D. Leann Yi, Nengjun Univ Alabama Birmingham Dept Biostat Birmingham AL 35294 USA Wake Forest Sch Med Dept Biostat & Data Sci Winston Salem NC 27101 USA

There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data (p >> n) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs consider an "all-in-all-out" approach for functional selection, rendering them difficult to answer if nonlinear effects are necessary. While some Bayesian models can address these shortcomings, using Markov chain Monte Carlo algorithms for model fitting creates a new challenge, scalability. Hence, we propose Bayesian hierarchical generalized additive models as a solution: we consider the smoothing penalty for proper shrinkage of curve interpolation via reparameterization. A novel two-part spike-and-slab LASSO prior for smooth functions is developed to address the sparsity of signals while providing extra flexibility to select the linear or nonlinear components of smooth functions. A scalable and deterministic algorithm, EM-Coordinate Descent, is implemented in an open-source R package BHAM. Simulation studies and metabolomics data analyses demonstrate improved predictive and computational performance against state-of-the-art models. Functional selection performance suggests trade-offs exist regarding the effect hierarchy assumption.

关键词： EM-coordinate decsent generalized additive models high-dimensional data predictive modeling scalablility spike-and-slab priors

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generalized additive models: An efficient method for short-term energy prediction in office buildings

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ENERGY 2020年 213卷

作者： Khamma, Thulasi Ram Zhang, Yuming Guerrier, Stephane Boubekri, Mohamed Univ Illinois Illinois Sch Architecture Champaign IL 61820 USA Univ Geneva Geneva Sch Econ & Management Geneva Switzerland Univ Geneva Fac Sci Geneva Switzerland

In 2018, commercial buildings accounted for nearly 18.2% of the total energy consumption in the USA, making it a significant contributor to the greenhouse gases emissions (see, e.g. [1]). Specifically, office buildings accounted for 14% of the energy usage by the commercial sector. Hence, their energy performance has to be closely monitored and evaluated to address the critical issues of greenhouse gases emissions. Several data-driven statistical and machine learning models have been developed to assess the energy performance of office buildings based on historical data. While these methods often provide reliable prediction accuracy, they typically offer little interpretation of the relationships between variables and their impacts on energy consumption. Moreover, model interpretability is essential to understand, control and manage the variables affecting the energy consumption and therefore, such a feature is crucial and should be emphasized in the modeling procedure in order to obtain reliable and actionable results. For this reason, we use generalized additive models as a flexible, efficient and interpretable alternative to existing approaches in modeling and predicting the energy consumption in office buildings. To demonstrate the advantages of this approach, we consider an application to energy consumption data of HVAC systems in a mixed-use multi-tenant office building in Chicago, Illinois, USA. We present the building characteristics and various influential variables, based on which we construct a generalized additive model. We compare the prediction performance using various commonly used calibration metrics between the proposed model and existing methods, including support vector machine as well as classification and regression tree. We find that the proposed method outperforms the existing approaches, especially in terms of short term prediction. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Building energy prediction Office buildings Energy performance generalized additive models Model calibration

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Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances

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TOURISM ECONOMICS 2022年第5期28卷 1377-1400页

作者： Weigert, Maximilian Bauer, Alexander Gernert, Johanna Karl, Marion Nalmpatian, Asmik Kuechenhoff, Helmut Schmude, Jurgen Ludwig Maximilians Univ Munchen Dept Stat Stat Consulting Unit StaBLab Ludwigstr 33 D-80539 Munich Germany Ludwig Maximilians Univ Munchen Dept Geog Munich Germany Ludwig Maximilians Univ Munchen Munich Germany Ludwig Maximilians Univ Munchen Dept Stat Munich Germany Ludwig Maximilians Univ Munchen Dept Geog Econ Geog & Tourism Res Munich Germany Univ Queensland Brisbane Qld Australia

This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971-2018 using a holistic age-period-cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.

关键词： APC analysis cohort analysis destination choice generalized additive models ridgeline matrices travel distance

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Identification of Mineralization in Geochemistry for Grid Sampling Using generalized additive models

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MATHEMATICAL GEOSCIENCES 2021年第8期53卷 1861-1880页

作者： Miksova, Dominika Rieser, Christopher Filzmoser, Peter Middleton, Maarit Sutinen, Raimo Vienna Univ Technol Inst Stat & Math Methods Econ Wiedner Hauptstr 8-10 A-1040 Vienna Austria Geol Survey Finland Environm Solut Unit Lahteentie 2POB 77 Rovaniemi 96101 Finland Geol Survey Finland Energy & Construct Solut Unit Lahteentie 2POB 77 Rovaniemi 96101 Finland

The important goals of mineral exploration geochemistry are detection and identification of underlying mineralization. This paper deals with element concentration data analyzed of surface geochemical samples acquired from soil horizons or plants. A new unsupervised procedure is proposed for this purpose when the samples have been taken on a regular or irregular grid in the area under investigation. The methodology is based on generalized additive Model fits on element concentration data. Then new data points are taken of the surface of the smooth fits across the entire sampling area as a regular grid. Pairwise log-ratios of elements are then calculated of these points, and curvature of the log-ratio pairs is computed. High curvature indicates abrupt spatial changes, which could point at locations of mineralized zones. A measure called c-value evaluates the overall curvature and thus serves as an importance measure of a log-ratio pair. The methodology is tested on two real surface geochemical data sets collected in areas with known underlying mineralization, and the results confirm existing knowledge of the underlying mineralization.

关键词： Compositional data Mineral identification Grid sampling generalized additive models Curvature

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STRUCTURAL BRAIN ATROPHY PREDICT SYMPTOM SEVERITY IN SCHIZOPHRENIA BASED ON generalized additive models 19

STRUCTURAL BRAIN ATROPHY PREDICT SYMPTOM SEVERITY IN SCHIZOP...

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19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI)

作者： Wang, Meng Fan, Lingzhong Liu, Bing Chinese Acad Sci Brainnetome Ctr Beijing Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing Peoples R China Chinese Inst Brain Res Beijing Peoples R China

ISBN: (纸本)9781665429238

Schizophrenia (SCZ) patients typically vary significantly in symptom severity. Despite numerous studies demonstrate SCZ is linked to brain structure abnormalities, relationships are obscure. In this paper, we establish relationships between structural abnormalities and symptom severity. All analyses are performed in two datasets (discovery: 326 SCZ and 298 normal control (NC);replication: 216 SCZ and 173 NC). We first build normative models in NC group, based on which we calculate atrophy values of cortical thickness, surface area, and gray matter volume in SCZ. Finally, we use atrophy values to predict symptom severity via generalized additive models and further evaluate the marginal effect of each structural feature. We found atrophy values could reliably predict symptom severity across two datasets (discovery: Pearson r = 0.29, P < 1 x 10(-5);replication: r = 0.26, P = 3 x10(-5)). Our findings could aid in understanding the pathogenesis of symptoms in SCZ.

关键词： Schizophrenia Prediction generalized additive models Symptom Severity Brain Atrophy

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Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

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ECOGRAPHY 2021年第12期44卷 1756-1768页

作者： McCabe, Jennifer D. Clare, John D. Miller, Tricia A. Katzner, Todd E. Cooper, Jeff Somershoe, Scott Hanni, David Kelly, Christine A. Sargent, Robert Soehren, Eric C. Threadgill, Carrie Maddox, Mercedes Stober, Jonathan Martell, Mark Salo, Thomas Berry, Andrew Lanzone, Michael J. Braham, Melissa A. McClure, Christopher J. W. Peregrine Fund Boise ID 83709 USA Univ Wisconsin Dept Forest & Wildlife Ecol Madison WI USA Conservat Sci Global West Cape May NJ USA West Virginia Univ Div Forestry & Nat Resources Morgantown WV 26506 USA US Geol Survey Boise ID USA Virginia Dept Wildlife Resources Richmond VA USA US Fish & Wildlife Serv Denver CO USA Tennesee Wildlife Resources Agcy Nashville TN USA North Carolina Wildlife Resources Commiss Asheville NC USA Georgia Dept Nat Resources Wildlife Resources Div Forsyth GA USA Wehle Land Conservat Ctr State Lands Div Alabama Dept Conservat & Nat Resources Midway AL USA Wehle Land Conservat Ctr State Lands Div Alabama Dept Conservat & Nat Resources Montgomery AL USA Shoal Creek Ranger Dist Talladega Natl Forest Heflin AL USA Minnesota Audubon St Paul MN USA Tetra Tech Bloomington MN USA Delaware Otsego Audubon Soc Oneonta NY USA Bernheim Arboretum & Res Forest Clermont KY USA Cellular Tracking Technol Rio Grande NJ USA

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with rando

关键词： Aquila chrysaetos generalized additive models hierarchical models prediction resource selection functions species distribution modeling

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