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Statistical Modeling of Opening Price Gaps in the Shanghai Stock Exchange Composite Index Using Linear Methods

COMPUTATIONAL ECONOMICS 2024年 1-36页

作者： Si, Yuancheng Nadarajah, Saralees Zhang, Zongxin Anhui Acad Social Sci Hefei Peoples R China Fudan Univ Sch Econ Shanghai Peoples R China Univ Manchester Dept Math Manchester England

This study explores the determinants of the opening price gap rate (diffrate) in the Chinese stock market using a range of statistical models, including linear models with regularization terms, generalized linear models (GLMs), generalized additive models (GAMs), and Long Short-Term Memory (LSTM) models. Emphasizing predictive accuracy, interpretability, and practical applicability, the findings reveal that GAMs with regularization terms outperform other approaches in forecasting opening price gap rate. The results identify several critical factors, including domestic market indices, global liquidity conditions, and market activity measures. By providing a comprehensive framework for understanding and modeling opening price dynamics, this work lays a robust foundation for future research and practical applications in market risk management and predictive analytics.

关键词： Opening price gap generalized linear models generalized additive models Regularization

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A new approach for monitoring healthcare performance using generalized additive profiles

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2021年第1期91卷 167-179页

作者： Erfanian, Mahdiyeh Sadeghpour Gildeh, Bahram Reza Azarpazhooh, Mahmoud Ferdowsi Univ Mashhad Dept Stat Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci MUMS Dept Neurol Ghaem Hosp Mashhad Razavi Khorasan Iran Western Univ Dept Clin Neurol Sci Schulich Sch Med & Dent London ON Canada

Recent evidence suggests ever-increasing applications of statistical process control (SPC) in health data analysis. However, the diversity in numbers and types of included variables warrant new statistical control charts. This inclusion can be improved by profiles that monitor a describing functional relationship of the process. In this article, we proposed multiple generalized additive models (GAMs) for profile construction. GAMs permit complex fitting models with simultaneous inclusions of parametric and nonparametric terms. Therefore, GAMs can be applied in health data monitoring with a wide range of explanatory variables. We used two statistics to build control charts: (1) a commonly used univariate statistic in nonparametric profiles;(2) a new proposed multivariate statistic which enables the chart to track the role of each included element in the process changes. The statistics are compared according to their performance in monitoring monthly stroke types, including ischaemic and haemorrhagic strokes of patients with acute stroke in the Mashhad Stroke Incidence Study. Features of the proposed profile are discussed and suggestions are made about the utilized statistics in process monitoring. The results show the successful performance of GAMs in profile monitoring.

关键词： Average run length generalized additive models healthcare performance monitoring multivariate control chart nonparametric profile monitoring statistical process control

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Modelling Energy Data in a generalized additive Model-A Case Study of Colombia

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ENERGIES 2023年第4期16卷 1929页

作者： Berbesi, Lina Pritchard, Geoffrey Univ Auckland Fac Sci Dept Stat Auckland 1010 New Zealand

Energy demand modelling is essential for reliable informing and framing energy policy decisions. More accurate modelling betters ensuring availability of energy and energy quality. Energy availability is related to energy access across the country and defines important economic measures such as energy poverty, which plays a critical role in developing countries. Energy quality is related to the reliability of the supply for correctly estimating energy needs. To incorporate spatial and temporal components of energy in a way that availability and quality are accurately assessed, this article discussed a number of suitable task methods for this (Second-generation GAMs with one-dimensional smoothers: Cyclic/Non-Cyclic Cubic Splines and two-dimensional smoothers: Markov Random Fields/Tensor Splines Interactions). The results showed that the complete consideration of both temporal and spatial aspects leads to a better fitted model which explains more of the data variation.

关键词： generalized additive models cubic splines Markov random fields tensor product splines energy modelling

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Social behavior in ASD males: The interplay between cognitive flexibility, working memory, and functional connectivity deviations

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DEVELOPMENTAL COGNITIVE NEUROSCIENCE 2025年 71卷 101483页

作者： Chan, Shi Yu Chuah, Jasmine Si Min Huang, Pei Tan, Ai Peng ASTAR Inst Human Dev & Potential IHDP 30 Med Dr Singapore 117609 Singapore Natl Univ Singapore NUS Yong Loo Lin Sch Med 10 Med Dr Singapore 117597 Singapore Natl Univ Hlth Syst Dept Diagnost Imaging 1E Kent Ridge Rd Singapore 119228 Singapore

Autism spectrum disorder (ASD) is highly heterogeneous in presentation. While abnormalities in brain functional connectivity are consistently observed in autistic males, the neurobiological basis underlying the different domains of autism symptoms is unclear. In this study, we evaluated whether individual variations in functional connectivity deviations map to social behavior in ASD males. Using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE), we modeled normative trajectories of between-network resting-state functional connectivity (rsFC) in non-ASD males across childhood (n = 321). These normative charts were then applied to ASD males (n = 418) to calculate individual deviation scores (z-scores) that reflect the degree of rsFC atypicality. Deviations in rsFC patterns among the default mode network (DMN), ventral attention network (VAN), frontoparietal network (FPN), and somatomotor network (SMN) were associated with distinct dimensions of social behavior. Cognitive flexibility and working memory mediated the association between VANxDMN z-scores and social behavioral problems. Our findings underscore the potential of normative models to identify atypical brain connectivity at an individual level, revealing the neurobiological patterns associated with social behavioral problems in ASD that are critical for precision diagnosis and intervention. Social outcomes in ASD males may be improved by targeting cognitive flexibility and working memory.

关键词： Autism spectrum disorders Functional connectivity generalized additive models Normative Trajectories Social behavior Executive functioning

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A highly variable habitat selection in moose across diel and seasonal scales

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MOVEMENT ECOLOGY 2024年第1期12卷 69页

作者： Borowik, Tomasz Kowalczyk, Rafal Ratkiewicz, Miroslaw Maslanko, Weronika Duda, Norbert Zmihorski, Michal Polish Acad Sci Mammal Res Inst Stoczek 1 PL-17230 Bialowieza Poland Univ Bialystok Fac Biol Ciolkowskiego 1 J PL-15245 Bialystok Poland Univ Life Sci Lublin Dept Anim Ethol & Wildlife Management Akad 13 PL-20950 Lublin Poland Zespol Szkol Ogolnoksztalcacych 2 BialymstokuNarewska 11 PL-15840 Bialystok Poland

PurposeHabitat selection in animals is a hierarchal process that operates across multiple temporal and spatial scales, adapting to changes in environmental conditions, human disturbances, and predation risks. Despite its significance, previous research often oversimplifies temporal dynamics by categorizing them into broad seasonal and diel patterns, overlooking the continuous nature of temporal variability and habitat *** investigated the temporal patterns in habitat selection of moose (Alces alces) in highly heterogenous landscapes at the southwestern edge of their European range using step-selection functions. Utilizing over 700,000 GPS locations from 34 adult moose, we aimed to assess seasonal and diel patterns in their selectivity for both natural and human-related *** findings revealed significant overall temporal variation in moose habitat selection at both seasonal and diel scales. Moose selectivity toward different habitats showed low repeatability over time, with 35% of cases displaying negative correlation between selectivity in different time windows. Diel changes were more pronounced, showing 5.6-fold difference in cumulative selectivity, compared to 1.4-fold difference in seasonal dynamics. Notably, moose exhibited lower selectivity during nighttime hours throughout the year compared to daytime hours. The study also highlighted distinct habitat selection patterns across different habitat types: natural habitats (deciduous forests, coniferous forests, wetlands) exhibited pronounced seasonal variation, while anthropogenic habitats (grasslands, arable land, roads and settlements) showed more diel variability. Moose generally avoided human-related habitats during daytime hours, but their preferences during nighttime varied depending on the habitat type and time of *** research advances our understanding of the complex temporal patterns in habitat selection by large herbivores and underscores the importance o

关键词： Temporal scales Habitat selection Step-selection function generalized additive models Moose

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Robust Transformations for Multiple Regression via Additivity and Variance Stabilization

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2024年第1期33卷 85-100页

作者： Riani, Marco Atkinson, Anthony C. Corbellini, Aldo Univ Parma Dipartimento Sci Econ & Aziendale Parma Italy Univ Parma Interdept Ctr Robust Stat Parma Italy London Sch Econ London England London Sch Econ London WC2A 2AE England

Outliers can have a major effect on the estimated transformation of the response in linear regression models, as they can on the estimates of the coefficients of the fitted model. The effect is more extreme in the generalized additive models (GAMs) that are the subject of this article, as the forms of terms in the model can also be affected. We develop, describe and illustrate robust methods for the nonparametric transformation of the response and estimation of the terms in the model. Numerical integration is used to calculate the estimated variance stabilizing transformation. Robust regression provides outlier free input to the polynomial smoothers used in the calculation of the response transformation and in the backfitting algorithm for estimation of the functions of the GAM. Our starting point was the AVAS (Additivity and VAriance Stabilization) algorithm of Tibshirani. Even if robustness is not required, we have made four further general optional improvements to AVAS which greatly improve the performance of Tibshirani's original Fortran *** provide a publicly available and fully documented interactive program for our procedure which is a robust form of Tibshirani's AVAS that allows many forms of robust regression. We illustrate the efficacy of our procedure through data analyses. A refinement of the backfitting algorithm has interesting implications for robust model selection. for this article are available online.

关键词： Augmented star plot AVAS Backfitting Forward search generalized additive models Robust model selection

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Bias correction of daily precipitation from climate models, using the Q-GAM method

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ENVIRONMETRICS 2024年第7期35卷

作者： Lazoglou, Georgia Economou, Theo Anagnostopoulou, Christina Tzyrkalli, Anna Zittis, George Lelieveld, Jos Cyprus Inst Climate & Atmosphere Res Ctr CARE C Nicosia Cyprus Univ Exeter Dept Math & Stat Exeter England Aristotle Univ Thessaloniki Sch Geol Dept Meteorol & Climatol Thessaloniki Greece Max Planck Inst Chem Dept Atmospher Chem Mainz Germany

Climate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q-GAM) for the bias correction of daily precipitation. Q-GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO-CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q-GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles.

关键词： bias adjustment dry days generalized additive models quantile mapping

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Multiple prenatal exposures and acute-care clinical encounters for asthma among children born to mothers living near a Superfund site

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2024年第8期193卷 1088-1096页

作者： Khalili, Roxana Legaspi, Jesselle M. Fabian, M. Patricia Levy, Jonathan, I Korrick, Susan A. Vieira, Veronica M. Univ Calif Irvine Susan & Henry Samueli Coll Hlth Sci Dept Environm & Occupat Hlth Program Publ Hlth 856 Hlth Sci Rd Irvine CA 92617 USA Univ Southern Calif MADRES Ctr Keck Sch Med Los Angeles CA 90032 USA Boston Univ Sch Publ Hlth Dept Environm Hlth Boston MA 02118 USA Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Brigham & Womens Hosp Dept Med Channing Div Network Med Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood acute-care clinical encounters for asthma (hospitalizations, emergency department visits, observational stays) using conditional logistic regression with a multivariable smoothing term to model the interaction between continuous variables, adjusted for maternal characteristics and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) in New Bedford, Massachusetts, were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System to identify children aged 5-11 years with acute-care clinical asthma encounters (265 cases among 7787 children with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH site with higher umbilical cord blood lead levels than in children living further away from the NBH site with lower lead levels (P <.001). HRs were higher for girls (HR = 4.17;95% CI, 3.60-4.82) than for boys (HR = 1.72;95% CI, 1.46-2.02). Our results suggest that prenatal lead exposure in combination with residential proximity to the NBH Superfund site is associated with childhood asthma acute-care clinical encounters. This article is part of a Special Collection on Environmental Epidemiology.

关键词： multiple exposures mixtures prenatal exposures generalized additive models childhood asthma birth cohort organochlorines lead

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More connected, more collisions? Documenting nonlinear relationships between habitat connectivity and wildlife-vehicle collision hotspots

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ENVIRONMENTAL MANAGEMENT 2025年 1-14页

作者： Dawson, Clara Villamagna, Amy M. Martin, Rebecca A. Moll, Remington J. Univ New Hampshire Dept Nat Resources & Environm Durham NH 03824 USA Plymouth State Univ Dept Environm Sci & Policy Plymouth NH USA New Hampshire Dept Transportat Concord NH USA

Road networks fragment wildlife habitat and impede wildlife connectivity, which leads to elevated wildlife-vehicle collision (WVC) risk and increased danger to humans and wildlife. Habitat connectivity has been linked to WVC hotspot location and intensity, but this relationship likely depends on landscape context and road characteristics, which may be nonlinear due to varying habitat availability. Our objective was to evaluate factors affecting WVC location and intensity across New Hampshire, USA, with a focus on habitat connectivity. We assessed the relationship between WVCs and five connectivity models using generalized additive models and compared connectivity effects to road and land cover characteristics. We found that a barrier-sensitive wildlife species connectivity model was the best predictor of WVC hotspots and had a strong, negative nonlinear relationship with collision intensity. We also found that a simple forest variable performed almost as well as the complex connectivity model. WVC hotspots did not differ from adjacent roads or regional roads in terms of connectivity, except that traffic volume was higher at hotspots. Our findings suggest that the relationship between habitat connectivity and WVCs depends on broader landscape context and likely exhibits nonlinearity. Our work also demonstrates that some connectivity models are better predictors of WVCs than others, emphasizing the role of species-specific habitat connectivity assessments. These results can inform WVC mitigation planning and enhance understanding of habitat connectivity's role in broader landscapes.

关键词： Connectivity Vehicle collisions Forest Traffic Mitigation generalized additive models

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A distributed algorithm for fitting generalized additive models

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OPTIMIZATION AND ENGINEERING 2013年第2期14卷 213-224页

作者： Chu, Eric Keshavarz, Arezou Boyd, Stephen Stanford Univ Dept Elect Engn Informat Syst Lab Stanford CA 94305 USA

generalized additive models are an effective regression tool, popular in the statistics literature, that provides an automatic extension of traditional linear models to nonlinear systems. We present a distributed algorithm for fitting generalized additive models, based on the alternating direction method of multipliers (ADMM). In our algorithm the component functions of the model are fit independently, in parallel;a simple iteration yields convergence to the optimal generalized additive model. This is in contrast to the traditional approach of backfitting, where the component functions are fit sequentially. We illustrate the method on different classes of problems such as generalized additive, logistic, and piecewise constant models, with various types of regularization, including those that promote smoothness and sparsity.

关键词： Convex optimization Distributed optimization generalized additive models

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