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Empirical Likelihood for generalized linear models with Longitudinal Data

Journal of Systems Science & Complexity 2023年第5期36卷 2100-2124页

作者： YIN Changming AI Mingyao CHEN Xia KONG Xiangshun School of Mathematics and Information Science Guangxi UniversityNanning 530004China LMAM School of Mathematical Sciences and Center for Statistical SciencePeking UniversityBeijing 100871China School of Mathematics and Statistics Shaanxi Normal UniversityXi'an 710062China School of Mathematics and Statistics Beijing Institute of TechnologyBeijing 100081China

generalized linear models are usually adopted to model the discrete or nonnegative *** this paper,empirical likelihood inference for fixed design generalized linear models with longitudinal data is *** some mild conditions,the consistency and asymptotic normality of the maximum empirical likelihood estimator are established,and the asymptotic χ^(2) distribution of the empirical log-likelihood ratio is also *** with the existing results,the new conditions are more weak and easy to *** simulations are presented to illustrate these asymptotic properties.

关键词： Empirical likelihood ratio generalized linear model longitudinal data maximum empirical likelihood estimator

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Sequential Feature Screening for generalized linear models with Sparse Ultra-High Dimensional Data

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Journal of Systems Science & Complexity 2020年第2期33卷 510-526页

作者： ZHANG Junying WANG Hang ZHANG Riquan ZHANG Jiajia Department of Mathematics Taiyuan University of TechnologyTaiyuan 030024China Department of Statistics East China Normal UniversityShanghai 200241China Department of Epidemiology and Biostatistics University of South CarolinaColumbia 20295USA

This paper considers the iterative sequential lasso(ISLasso)variable selection for generalized linear model with ultrahigh dimensional feature *** ISLasso selects features by estimated parameter sequentially iteratively for the second order approximation of likelihood function where the features selected depend on regulatory *** procedure stops when extended BIC(EBIC)reaches a *** study demonstrates that the new method is a desirable approach over other methods.

关键词： Extended BIC generalized linear model sequential lasso sequential iteration variable screening variable selection

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Assessing Racial and Ethnic Health Disparities in a Diverse Cohort with Pediatric Inflammatory Bowel Disease

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JOURNAL OF PEDIATRICS 2025年 280卷 114504-114504页

作者： Watson, Ashleigh Young, Chelsea Ihekweazu, Faith D. Texas Childrens Hosp Baylor Coll Med Dept Pediat Gastroenterol Hepatol & Nutr Houston TX USA

Objective To identify racial and ethnic disparities in disease phenotype, treatment, and outcome in a diverse cohort of children with pediatric inflammatory bowel disease (IBD). Study design Patients aged 7 through 18 with IBD diagnosed at a single institution between March 2020 and June 2021 with self- or parent-identified race and ethnicity of non-Hispanic (NH) Black, NH-White, or Hispanic were included. Demographics, Centers for Disease Control/Agency for Toxic Substances and Disease Registry Social Vulnerability Index, Childhood Opportunity Index, disease phenotype, time to diagnosis, treatment, and health care utilization were compared between the racial and ethnic groups. Results Ninety-seven patients were included. A total of 18.6% of the cohort self- or parent-identified as NH-Black, 53.6% as NH-White, and 27.8% as Hispanic. Ulcerative colitis was found to be significantly more common in Hispanic patients. Hispanic patients were also significantly more likely to be hospitalized at time of diagnosis and have more emergency department visits within 2 years of diagnosis compared with non-Hispanic White patients. Conclusions Race and ethnicity may affect the diagnosis and treatment of pediatric IBD, and these findings should serve as a foundation for establishing equitable care. Larger cohorts are needed to validate these findings.

关键词： IBD ulcerative colitis Crohn's disease African American black latinx caucasian bias socioeconomic diversity income equality CD Crohn's disease COI Childhood Opportunity Index ED Emergency department GI Gastroenterology GLM generalized linear model IBD Inflammatory bowel disease IBD-U IBD-unclassified NH Non-Hispanic SDH Social determinants of health SVI Social Vulnerability Index TCH Texas Children's Hospital UC Ulcerative colitis

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Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models

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BIOSYSTEMS 2025年 247卷 105360页

作者： Garcia, Juan A. Bouchnita, Anass Univ Texas El Paso Dept Math Sci El Paso TX 79968 USA

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significantly larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.

关键词： Neural networks Random forests generalized linear model Brownian dynamics Discrete model Intracellular signaling

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Covariance matrix formula for generalized linear models with unknown dispersion

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2006年第1期35卷 113-120页

作者： Cordeiro, GM Barroso, LP Botter, DA Univ Sao Paulo Dept Estatist BR-05508090 Sao Paulo Brazil Univ Fed Rural Pernambuco Dept Estatist & Informat Recife PE Brazil

This article gives a matrix formula for second-order covariances of maximum likelihood estimators in generalized linear models when the dispersion parameter is unknown, thus generalizing the result of Cordeiro (2004). We show that the second-order covariances when the dispersion is unknown are the covariances obtained by this author plus some extra terms. We also give these covariances for some models. Some simulations show that the second-order covariances can be quite pronounced in small to moderate sample sizes.

关键词： canonical model covariance matrix dispersion parameter generalized linear model maximum likelihood estimate precision parameter

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modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks,random forest,and generalized linear models

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Ecological Processes 2023年第1期12卷 151-164页

作者： Girma Ayele Bedane Gudina Legese Feyisa Feyera Senbeta Wakjira Center for Environmental Science College of Computational and Natural ScienceAddis Ababa UniversityP.O.Box 1176Addis AbabaEthiopia Centre for Environment and Development College of Development StudiesAddis Ababa UniversityAddis AbabaEthiopia

Background Abiotic factors exert different impacts on the abundance of individual tree species in the forest but little has been known about the impact of abiotic factors on the individual plant,particularly,in a tropical *** study identified the impact of abiotic factors on the abundances of Podocarpus falcatus,Croton macrostachyus,Celtis africana,Syzygium guineense,Olea capensis,Diospyros abyssinica,Feliucium decipenses,and Coffea arabica.A systematic sample design was used in the Harana forest,where 1122 plots were established to collect the abundance of *** forest(RF),artificial neural network(ANN),and generalized linear model(GLM)models were used to examine the impacts of topographic,climatic,and edaphic factors on the log abundances of woody *** RF model was used to predict the spatial distribution maps of the log abundances of each *** The RF model achieved a better prediction accuracy with R^(2)=71%and a mean squared error(MSE)of 0.28 for Feliucium *** RF model differentiated elevation,temperature,precipitation,clay,and potassium were the top variables that influenced the abundance of *** ANN model showed that elevation induced a nega-tive impact on the log abundances of all woody *** GLM model reaffirmed the negative impact of elevation on all woody species except the log abundances of Syzygium guineense and Olea *** ANN model indicated that soil organic matter(SOM)could positively affect the log abundances of all woody *** GLM showed a similar positive impact of SOM,except for a negative impact on the log abundance of Celtis africana at p<*** spatial distributions of the log abundances of Coffee arabica,Filicium decipenses,and Celtis africana were confined to the eastern parts,while the log abundance of Olea capensis was limited to the western *** The impacts of abiotic factors on the abundance of woody species may vary with *** ecological underst

关键词： Species distribution model Random forest Artificial neural network generalized linear model Species abundance Woody species Environmental variable

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Approximate Conditional Likelihood for generalized linear models with General Missing Data Mechanism

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Journal of Systems Science & Complexity 2017年第1期30卷 139-153页

作者： ZHAO Jiwei SHAO Jun Department of Biostatistics State University of New York at Buffalo School of Statistics East China Normal University

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and *** this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.

关键词： Asymptotic normality generalized linear model identifiability missing data mechanism,non-canonical link function nonignorable missingness.

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Optimal knots for smoothing splines and adaptive smoothing splines in generalized linear models: Design approach

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2024年第12期54卷 3672-3691页

作者： Allami, Mina Niaparast, Mehrdad Razi Univ Dept Stat Kermanshah Iran

Smoothing splines play a crucial role as a prevalent tool for estimating unknown functions in non parametric regression. In fitting functions with varying roughness, adaptive smoothing splines provide the flexibility to adjust the smoothing parameter adaptively with the changes over the predictor variable within specific intervals, facilitating accommodation for changing roughness. Despite its intuitive appeal and high flexibility, the implementation of this estimator poses challenges, especially in the absence of specified knot numbers, knot locations, and certain simplifying assumptions on the smoothing parameter. D-optimal designs are considered to specify the knot locations in this models. The prior information from data as the prior curvature knowledge can be useful for this optimization problem. For this purpose, the smoothing parameter in this optimization problem can be considered as a function based on the prior curvature. We apply this method to responses from the exponential family of distributions. The mathematical expression of the estimator of the curve in a generalized linear mixed model enables us to derive an information matrix. The derived information matrix and hence the optimum design will depend on the values of the model's unknown parameters. In such situations, numerical techniques should be considered for evaluating and optimizing this criterion.

关键词： generalized linear model optimal design smoothing splines adaptive smoothing splines penalaized likelihood

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ASYMPTOTIC NORMALITY OF QUASI MAXIMUM LIKELIHOOD ESTIMATE IN generalized linear modelS

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Chinese Annals of Mathematics,Series B 2005年第3期26卷 467-474页

作者： YUELI CHENXIRU SchoolofMathematicsandStatistics WuhanUniversityWuhan430072China GraduateSchooloftheChineseAcademyofSciences Beijing100049China

For the generalized linear model (GLM), under some conditions including that the specification of the expectation is correct, it is shown that the Quasi Maximum Likelihood Estimate (QMLE) of the parameter-vector is asymptotic normal. It is also shown that the asymptotic covariance matrix of the QMLE reaches its minimum (in the positive-definte sense) in case that the specification of the covariance matrix is correct.

关键词： Quasi likelihood estimate generalized linear model Asmptotically normal Asymptotic normality

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Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2001年第2期38卷 217-233页

作者： Meulders, M De Boeck, P Van Mechelen, I Univ Louvain Dept Psychol B-3000 Louvain Belgium

The present paper deals with main-effects models for the analysis of two-way two-mode frequency data that are obtained by summing replicated binary associations over replications. Main-effects generalized linear models (GLM) which explain the transformed expected frequencies as an additive function of row- and column parameters, include many models that only differ in the particular transformation-called link function-that is applied, In this paper, besides the standard logit link, we also consider a log-, a complementary log-, and an identity link as models with these link functions are equivalent to one-feature probability matrix decomposition models (PMD). Reparameterizing main-effects GLMs into one-feature PMD models yields two advantages: First, it offers a process interpretation for the way (he observed data are generated;in particular, in PMD models it is assumed that covert events with binary outcomes take place on both the row- and the column side and that these outcomes are subsequently mapped into an observed data point according to a prespecified mapping rule which may reflect a communality or a dominance relation. Second, it may facilitate parameter estimation of these models as for the original parameterization maximum likelihood estimates in the interior of the parameter space may not exist. Methods to evaluate the goodness of fit of the models under study are discussed and the models are applied to data on psychiatric diagnosis and on educational measurement. (C) 2001 Elsevier Science B.V, All rights reserved.

关键词： discrete data matrix decomposition generalized linear model main-effects model link function

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