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INFERENCE FOR THRESHOLD modelS WITH VARIANCE-COMPONENTS FROM THE generalized linear MIXED-model PERSPECTIVE

GENETICS SELECTION EVOLUTION 1995年第1期27卷 15-32页

作者： ENGEL, B BUIST, W VISSCHER, A DLO INST ANIM SCI & HLTH3700 AM ZEISTNETHERLANDS

The analysis of threshold models with fixed and random effects and associated variance components is discussed from the perspective of generalized linear mixed models (GLMMs). Parameters are estimated by an interative procedure, referred to as iterated re-weighted REML (IRREML). This procedure is an extension of the iterative re-weighted least squares algorithm for generalized linear models. An advantage of this approach is that it immediately suggests how to extend ordinary mixed-model methodology to GLMMs. This is illustrated for lambing difficulty data. IRREML can be implemented with standard software available for ordinary normal data mixed models. The connection with other estimation procedures, eg, the maximum a posteriori (MAP) approach, is discussed. A comparison by simulation with a related approach shows a distinct pattern of the bias of MAP and IRREML for heritability. When the number of fixed effects is reduced, while the total number of observations is kept about the same, bias decreases from a large positive to a large negative value, seemingly independently of the sizes of the fixed effects.

关键词： BINOMIAL DATA THRESHOLD model VARIANCE COMPONENTS generalized linear model RESTRICTED MAXIMUM LIKELIHOOD

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THE ANALYSIS OF BINOMIAL DATA BY A generalized linear MIXED model

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BIOMETRIKA 1985年第3期72卷 593-599页

作者： GILMOUR, AR ANDERSON, RD RAE, AL MASSEY UNIV DEPT ANIM SCI PALMERSTON NORTH NEW ZEALAND

Methods for generalized linear models are extended to provide estimates of location and variance parameters for mixed models fitted to binomial data formed by classifying samples from an underlying normal distribution. The method estimates the parameters directly on the underlying scale. For a balanced one-way random effects model, the variance estimator simplifies to the usual analysis of variance one. The estimation of variances and the prediction of random effects for binomial traits is required by animal breeders. The predictors given are analogous to best linear unbiased predictors (Henderson, 1973) but differ from those presented by Harville and Mee (1984).

关键词： Binomials Statistical variance Estimators Approximation generalized linear model Maximum likelihood estimation Animal genetics Heritability Estimate reliability Family size

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Use of generalized linear Bayesian model to mitigate the impact of spatial contraction in fishing pattern on the estimation of relative abundance

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FISHERIES RESEARCH 2010年第3期106卷 413-419页

作者： Zhang, Zane Holmes, John Pacific Biol Stn Nanaimo BC V9T 6N7 Canada

We developed a hierarchical Bayesian generalized linear model (Delta model) for CPUE standardization and tested this model with Japanese longline fisheries data from 1975 to 2006 for south Pacific Ocean albacore tuna (Thunnus alalunga) The Delta model consists of binomial and lognormal sub-models and was developed to predict catch rates for areas bypassed by the fishery in some years Incorporation of these predicted catch rates into the standardization process mitigates the impact of spatial contractions in fishing pattern on the estimation of abundance indices The relative abundance of albacore as measured by the standardized and nominal CPUEs are similar for the early years when the spatial pattern of the fishing did not change However when the spatial coverage of fishing was decreasing the standardized CPUEs without predicted catch rates and nominal CPUEs were similar in the first half of this period but the standardized CPUE was lower than nominal CPUE in the remaining years of this period In contrast standardized CPUEs with the inclusion of predicted catch rates indicated significantly lower abundances than the nominal CPUEs for this entire period Explanatory variables considered in this study were Year Month Area Year-Area interaction and Depth using hooks per basket (HPB) as a proxy variable All variables except for HPB were important in explaining variations in catch rates Comparison of model predicted probabilities and observed proportions of non-zero catches showed that the binomial sub-model fit was adequate and calculated Bayesian p-values indicated that the lognormal sub-model fit was acceptable We also show that model fitting can be improved by adding random process error into the relationship between the explanatory variables and the function of the expected values of the response variables Crown Copyright (C) 2010 Published by Elsevier B V All rights reserved

关键词： CPUE standardization Bayesian generalized linear model Spatial contraction

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Evaluation of frequentist test statistics using constrained statistical inference in the context of the generalized linear model

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HEALTH PSYCHOLOGY AND BEHAVIORAL MEDICINE 2023年第1期11卷 2222164页

作者： Keck, Caroline Mayer, Axel Rosseel, Yves Univ Ghent Fac Psychol & Educ Sci Dept Data Anal Henri Dunantlaan 1 B-9000 Ghent Belgium Bielefeld Univ Psychol Methods & Evaluat Bielefeld Germany

When faced with a binary or count outcome, informative hypotheses can be tested in the generalized linear model using the distance statistic as well as modified versions of the Wald, the Score and the likelihood-ratio test (LRT). In contrast to classical null hypothesis testing, informative hypotheses allow to directly examine the direction or the order of the regression coefficients. Since knowledge about the practical performance of informative test statistics is missing in the theoretically oriented literature, we aim at closing this gap using simulation studies in the context of logistic and Poisson regression. We examine the effect of the number of constraints as well as the sample size on type I error rates when the hypothesis of interest can be expressed as a linear function of the regression parameters. The LRT shows the best performance in general, followed by the Score test. Furthermore, both the sample size and especially the number of constraints impact the type I error rates considerably more in logistic compared to Poisson regression. We provide an empirical data example together with R code that can be easily adapted by applied researchers. Moreover, we discuss informative hypothesis testing about effects of interest, which are a non-linear function of the regression parameters. We demonstrate this by means of a second empirical data example.

关键词： Informative hypothesis testing constrained statistical inference type I error rates generalized linear model treatment effects

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FUSED KERNEL-SPLINE SMOOTHING FOR REPEATEDLY MEASURED OUTCOMES IN A generalized PARTIALLY linear model WITH FUNCTIONAL SINGLE INDEX

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ANNALS OF STATISTICS 2015年第5期43卷 1929-1958页

作者： Jiang, Fei Ma, Yanyuan Wang, Yuanjia Harvard Univ Sunnyvale CA 94085 USA Univ S Carolina Columbia SC 29208 USA Columbia Univ New York NY 10032 USA

We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show a different convergence rate for each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work, even in the independent data case.

关键词： B-spline generalized linear model Huntington's disease infinite dimension logistic model semiparametric model single index model

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An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

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Heliyon 2024年第18期10卷 e38027页

作者： Ghislain, Feudjio Beaudelaire, Saha Tchinda Daniel, Tchiotsop Research Unit of Condensed Matter Electronics and Signal Processing (UR-MACETS). Department of Physics Faculty of Sciences University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer (UR-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box: 134 Bandjoun Cameroon

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a generalized linear model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets. © 2024 The Authors

关键词： Curvelet transform Gabor filters generalized linear model Image segmentation Retinal blood vessel

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Estimation of actual evapotranspiration in barley crop through a generalized linear model

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METHODSX 2022年 9卷 101665页

作者： Faraminan, Adan Carmona, Facundo Holzman, Mauro Rivas, Raul Mancino, Christian Rodriguez, Paula Olivera Consejo Nacl Invest Cient & Tecnicas Inst Hidrol Llanuras Dr Eduardo J Usunoff IHLLA Rep Italia 780B7300 Azul Argentina Comis Invest Cient Prov Buenos Aires Inst Hidrol Llanuras Dr Eduardo J Usunoff IHLLA B7000 Tandil Argentina

Evapotranspiration is a key variable of the water cycle. Its calculation requires several ground data that frequently are not available. This study contains a detailed method and measurements of meteorological and energy balance variables that can be used to estimate the daily actual evapotranspiration (ETa). A linear generalized model is obtained to calculate the ETa from common variables measured in meteorological stations. The method showed a good performance over a barley crop of easthern Argentine Pampas and can be applied and tested in other great plains. Measurements of soil-plant-atmosphere are included The routines to reproduce the method are included The generalized method allows the calculation of daily ETa over crops and was tested over barley crops (c) 2022 Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Soil moisture surface energy balance generalized linear model

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The Poisson inverse Gaussian (PIG) generalized linear regression model for analyzing motor vehicle crash data

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JOURNAL OF TRANSPORTATION SAFETY & SECURITY 2016年第1期8卷 18-35页

作者： Zha, Liteng Lord, Dominique Zou, Yajie Texas A&M Univ Zachry Dept Civil Engn College Stn TX USA Univ Washington Dept Civil & Environm Engn Seattle WA USA

This article documents the application of the Poisson inverse Gaussian (PIG) regression model for modeling motor vehicle crash data. The PIG distribution, which mixes the Poisson distribution and inverse Gaussian distribution, has the potential for modeling highly dispersed count data due to the flexibility of inverse Gaussian distribution. The objectives of this article were to evaluate the application of PIG regression model for analyzing motor vehicle crash data and compare the results with negative binomial (NB) model, especially when varying dispersion parameter is introduced. To accomplish these objectives, NB and PIG models were developed with fixed and varying dispersion parameters and compared using two data sets. The results of this study show that PIG models perform better than the NB models in terms of goodness-of-fit statistics. Moreover, the PIG model can perform as well as the NB model in capturing the variance of crash data. Lastly, PIG models demonstrate almost the same prediction performance compared to NB models. Considering the simple form of PIG model and its easiness of applications, PIG model could be used as a potential alternative to the NB model for analyzing crash data.

关键词： Poisson-inverse Gaussian Poisson-gamma generalized linear model traffic crashes

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Quantifying the Impact of Climatic Factors on Dengue Incidence Using generalized linear Mixed model with Spatio-Temporal Bayesian Poisson Random Effects Approach 6th

Quantifying the Impact of Climatic Factors on Dengue Inciden...

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6th International Conference on Soft Computing in Data Science (SCDS)

作者： Sapri, Nik Nur Fatin Fatihah Yaacob, Wan Fairos Wan Yap, Bee Wah Univ Teknol MARA Fac Comp & Math Sci Shah Alam 40450 Selangor Malaysia Univ Teknol MARA Cawangan Kelantan Fac Comp & Math Sci Kota Baharu 15050 Kelantan Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Kompleks Al Khawarizmi Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9789811673344;9789811673337

Dengue fever is a global life-threatening vector-borne disease which is mainly distributed by the vector Aedes Aegypti mosquito. It is known that the development and survivorship of this vector depends on surrounding climate. The dengue outbreak in Kelantan, Malaysia is alarming. The aim of the study was to compare the fixed effects Negative Binomial GLM and Bayesian Poisson GLMM in prediction of dengue incidence in Kelantan. The data involved daily number of reported dengue cases (1st January 2013-31st December 2017) in ten districts of Kelantan which was collected from Ministry of Health Malaysia. The climate variables, average daily temperature, relative humidity and rainfall (climatic data) were obtained from NASA's Global Climate Change website, while the population data were from Department of Statistics Malaysia. Statistical modeling results revealed that the fixed effects Negative Binomial GLM failed to fit the daily dengue incidence when serious epidemic occurred. The spatio-temporal Bayesian Poisson GLMM model improved the prediction of dengue incidence. Relative humidity at lag 7 days and 21 days and average temperature at lag 21 days were found to be significant contributing factors of dengue incidence in Kelantan. The findings of the study are significant to respective local authorities in providing vital information for early dengue warning systems in a particular area. This is important for authorities to monitor and reduce dengue incidence in endemic areas and to safeguard the community from dengue outbreak.

关键词： Dengue generalized linear model generalized linear Mixed model Spatio-Temporal Bayesian Poisson model Climate

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Gibbs-Sampler approach for meta-analysis of multiple clinical trials using generalized linear model with random-effects

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中华医学杂志 2000年第6期113卷 554-554页

作者： WANG Yong WANG Yuehe LI Linxian Department of Medicine McMaster University Ontario Canada China 650031 Clinical Trials &amp Evaluation National Heart &amp Lung Institute London UK

OBJECTIVE: To investigate the use of the Gibbs-Sampler method in evaluating the relationship between clinic events and health risks in a meta-analysis of multiple clinical trials. METHODS: By using a generalized linear model with random-effects, Gibbs-Sampler technique was used in a meta-analysis of multiple clinical trials of angiotensin converting enzyme (ACE) inhibitors in patients with myocardial infarction (MI). RESULTS: When heterogeneity across different trials can not be ignored, compared with the classic method, the odds ratio of relative reinfarction risk estimated by the Gibbs-Sampler method would have less variation. The gain in the reduction of variation in estimate of the overall odds ratio was 9.52%. CONCLUSION: Implementation of the Gibbs-Sampler technique in meta-analysis of multiple clinical trials has the potential of reducing the inaccuracy caused by heterogeneity across trials.

关键词： generalized linear model

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