检索结果-内蒙古大学图书馆

Permutation-based variance component test in generalized linear mixed model with application to multilocus genetic association study

引用

BMC MEDICAL RESEARCH METHODOLOGY 2015年第1期15卷 1-9页

作者： Zeng, Ping Zhao, Yang Li, Hongliang Wang, Ting Chen, Feng Nanjing Med Univ Sch Publ Hlth Dept Epidemiol & Biostat Nanjing 211166 Jiangsu Peoples R China Xuzhou Med Coll Sch Publ Hlth Ctr Med Stat & Data Anal Dept Epidemiol & Biostat Xuzhou 221004 Jiangsu Peoples R China Ctr Dis Control & Prevent Pudong New Area Shanghai 200136 Peoples R China

Background: In many medical studies the likelihood ratio test (LRT) has been widely applied to examine whether the random effects variance component is zero within the mixed effects models framework;whereas little work about likelihood-ratio based variance component test has been done in the generalized linear mixed models (GLMM), where the response is discrete and the log-likelihood cannot be computed exactly. Before applying the LRT for variance component in GLMM, several difficulties need to be overcome, including the computation of the log-likelihood, the parameter estimation and the derivation of the null distribution for the LRT statistic. Methods: To overcome these problems, in this paper we make use of the penalized quasi-likelihood algorithm and calculate the LRT statistic based on the resulting working response and the quasi-likelihood. The permutation procedure is used to obtain the null distribution of the LRT statistic. We evaluate the permutation-based LRT via simulations and compare it with the score-based variance component test and the tests based on the mixture of chi-square distributions. Finally we apply the permutation-based LRT to multilocus association analysis in the case-control study, where the problem can be investigated under the framework of logistic mixed effects model. Results: The simulations show that the permutation-based LRT can effectively control the type I error rate, while the score test is sometimes slightly conservative and the tests based on mixtures cannot maintain the type I error rate. Our studies also show that the permutation-based LRT has higher power than these existing tests and still maintains a reasonably high power even when the random effects do not follow a normal distribution. The application to GAW17 data also demonstrates that the proposed LRT has a higher probability to identify the association signals than the score test and the tests based on mixtures. Conclusions: In the present paper the permutation-base

关键词： Likelihood ratio test Permutation procedure Variance component Multilocus association analysis Working response Penalized quasi-likelihood algorithm generalized linear mixed model Logistic mixed effects model

来源：评论

学校读者我要写书评

暂无评论

Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model

引用

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2004年第3期167卷 501-517页

作者： Bailey, TC Hewson, PJ Univ Exeter Dept Math Sci Exeter EX4 4QE Devon England

Traffic safety in the UK is one of the increasing number of areas where central government sets targets based on 'outcome-focused' performance indicators (PIs). Judgments about such PIs are often based solely on rankings of raw indicators and simple league tables dominate centrally published analyses. There is a considerable statistical literature examining health and education issues which has tended to use the generalized linear mixed model (GLMM) to address variability in the data when drawing inferences about relative performance from headline PIs. This methodology could obviously be applied in contexts such as traffic safety. However, when such models are applied to the fairly crude data sets that are currently available, the interval estimates generated, e.g. in respect of rankings, are often too broad to allow much real differentiation between the traffic safety performance of the units that are being considered. Such results sit uncomfortably with the ethos of 'performance management' and raise the question of whether the inference from such data sets about relative performance can be improved in some way. Motivated by consideration of a set of nine road safety performance indicators measured on English local authorities in the year 2000, the paper considers methods to strengthen the weak inference that is obtained from GLMMs of individual indicators by simultaneous, multivariate modelling of a range of related indicators. The correlation structure between indicators is used to reduce the uncertainty that is associated with rankings of any one of the individual indicators. The results demonstrate that credible intervals can be substantially narrowed by the use of the multivariate GLMM approach and that multivariate modelling of multiple PIs may therefore have considerable potential for introducing more robust and realistic assessments of differential performance in some contexts.

关键词： generalized linear mixed model Markov chain Monte Carlo methods multivariate modelling performance indicators road casualties traffic safety

来源：评论

学校读者我要写书评

暂无评论

A non-stationary spatial generalized linear mixed model approach for studying plant diversity

引用

JOURNAL OF APPLIED STATISTICS 2011年第9期38卷 1935-1950页

作者： Majumdar, Anandamayee Gries, Corinna Walker, Jason Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA Univ Wisconsin Ctr Limnol Madison WI 53706 USA Arizona State Univ Sch Life Sci Tempe AZ 85287 USA

We analyze the multivariate spatial distribution of plant species diversity, distributed across three ecologically distinct land uses, the urban residential, urban non-residential, and desert. We model these data using a spatial generalized linear mixed model. Here plant species counts are assumed to be correlated within and among the spatial locations. We implement this model across the Phoenix metropolis and surrounding desert. Using a Bayesian approach, we utilized the Langevin-Hastings hybrid algorithm. Under a generalization of a spatial log-Gaussian Cox model, the log-intensities of the species count processes follow Gaussian distributions. The purely spatial component corresponding to these log-intensities are jointly modeled using a cross-convolution approach, in order to depict a valid cross-correlation structure. We observe that this approach yields non-stationarity of the model ensuing from different land use types. We obtain predictions of various measures of plant diversity including plant richness and the Shannon-Weiner diversity at observed locations. We also obtain a prediction framework for plant preferences in urban and desert plots.

关键词： cross convolution cross-covariance matrix generalized linear mixed model Langevin-Hastings algorithm log-Gaussian Cox model Markov chain Monte Carlo multivariate spatial model

来源：评论

学校读者我要写书评

暂无评论

A computationally efficient model selection in the generalized linear mixed model

引用

COMPUTATIONAL STATISTICS 2010年第3期25卷 463-484页

作者： Yoshida, Takuma Kanba, Masaru Naito, Kanta Shimane Univ Dept Math Matsue Shimane 6908504 Japan Shimane Univ Grad Sch Sci & Engn Matsue Shimane 6908504 Japan

This paper is concerned with model selection in spline-based generalized linear mixed model. Exploiting the fact that smoothing parameters can be expressed as the reciprocal ratio of the variances of random effect under the setting of estimation by regularization, we propose a computationally efficient model selection procedure. Applications to some real data sets reveal that the proposed method selects reasonable models and is very fast to implement.

关键词： BLUP generalized linear mixed model GIC model selection Penalized quasi likelihood Smoothing parameter Spline

来源：评论

学校读者我要写书评

暂无评论

Researcher capacity estimation based on the Q model: a generalized linear mixed model perspective

引用

SCIENTOMETRICS 2023年第8期128卷 4753-4764页

作者： Forthmann, Boris Univ Munster Inst Psychol Munster Germany

Chance models of scientific creative productivity allow estimation of researcher capacity. One prominent such model is the Q model in which the impact of a scholarly work is modeled as a multiplicative function of researcher capacity and a potential impact (i.e., luck) parameter. Previous work estimated researcher capacity based on an approximation of the Q parameter. In this work, however, I outline how the Q model can be estimated within the framework of generalized linear mixed models. This way estimates of researcher capacity (and all other parameters of the Q model) are readily available and obtained by standard statistical software packages. Usage of such software further allows comparing different distributional assumptions and calculation of reliability of the Q parameter (i.e., researcher capacity). This is illustrated for a large dataset of multidisciplinary scientists (N = 20,296). The Poisson Q model was found to have negligibly better predictive accuracy than the original normal Q model. Reliability estimates revealed excellent reliability of Q estimates with conditional reliability being mostly in acceptable ranges. Reliability of Q parameter estimates further depended heavily on the number of publications of a scientist with reliability increasing with the number of papers. The future and limitations of the Q model in the context of researcher capacity estimation are thoroughly discussed.

关键词： Q model Researcher capacity generalized linear mixed model

来源：评论

学校读者我要写书评

暂无评论

A generalized linear mixed model analysis of a multi-vessel fishery resource survey

引用

FISHERIES RESEARCH 2004年第2-3期70卷 251-264页

作者： Helser, TE Punt, AE Methot, RD NW Fisheries Sci Ctr Natl Marine Fisheries Serv Natl Ocean & Atmospher Adm 2725 Montlake Blvd E Natori Miyagi 98112 Japan Univ Washington Sch Aquat & Fishery Sci Seattle WA 98195 USA

A generalized linear mixed model (GLMM) that treats year and spatial cell as fixed effects while treating vessel as a random effect is used to examine fishing power among chartered industry-based vessels and a research trawler, the FRV Miller Freeman, for bottom trawl surveys on the upper continental slope of U.S. West coast. A Bernoulli distribution is used to model the probability of a non-zero haul and the gamma distribution to model the non-zero catch rates of four groundfish species. The use of vessel as a random effect allows the data for the various vessels to be combined and a single continuous time-series of biomass indices to be developed for stock assessment purposes. The GLMMs fit the data reasonably well. Among the different models examined, the GLMM incorporating a random vessel x year effect had the smallest DeltaAIC and was thus chosen as the best model. Also, estimated random effects coefficients associated with the industry-based vessels and the FRV Miller Freeman for each year suggests that these vessels can be assumed to be from a common random effects distribution. These results suggest that combining data from the chartered industry-based vessels and from the research trawler may be appropriate to develop indices of abundance for stock assessment purposes. Finally, an evaluation of variances associated with abundance indices from the different models indicate that analyzing these data as a fixed effect GLM may underestimate the level of variability due to ignoring the grouped nature of tows within vessels. As such, use of a mixed model approach with vessel as a random effect is a reasonable approach to developing abundance indices and their variances. (C) 2004 Elsevier B.V. All rights reserved.

关键词： trawl survey generalized linear mixed model delta distribution gamma distribution

来源：评论

学校读者我要写书评

暂无评论

Use of a generalized linear mixed model to reduce excessive heterogeneity in petroleum spray oil bioassay data

引用

JOURNAL OF ECONOMIC ENTOMOLOGY 2003年第3期96卷 983-989页

作者： Barchia, IM Herron, GA Gilmour, AR NSW Agr Elizabeth Macarthur Agr Inst Camden NSW 2570 Australia NSW Agr Orange Agr Inst Orange NSW 2800 Australia

High heterogeneity (variance) is a consistent and significant problem in petroleum spray oil derived bioassay data. It can mask small statistical differences sought by researchers in relative toxicity or potency analysis. To compensate for excessive heterogeneity, researchers often use very large sample sizes to improve statistical accuracy. We present a statistical method of modeling heterogeneity extending the conventional probit model by adding random effects to it. We illustrate this by reanalyzing 26 of our own published experiments. Twelve of these had excessive heterogeneity that was significantly reduced in ten cases by including random replicate effects with or without random slopes. Five were further improved by allowing a nonlinear (spline) response. The result was tighter confidence intervals for the estimates of lethal dose.

关键词： bioassay extra binomial variation probit analysis generalized linear mixed model spray oils

来源：评论

学校读者我要写书评

暂无评论

Multiple Group Comparisons of the Fixed and Random Effects From the generalized linear mixed model

引用

SOCIOLOGICAL METHODS & RESEARCH 2024年第1期53卷 448-504页

作者： Kasper, Daniel Schulz-Heidorf, Katrin Schwippert, Knut Univ Hamburg Dept Gen Intercultural & Int Comparat Educ Fac Educ Von Melle Pk 8 D-20146 Hamburg Germany

In this article, we extend Liao's test for across-group comparisons of the fixed effects from the generalized linear model to the fixed and random effects of the generalized linear mixed model (GLMM). Using as our basis the Wald statistic, we developed an asymptotic test statistic for across-group comparisons of these effects. The test can be applied when the fixed and random effects are multivariate normally distributed, and it works well for any link function and conditional distribution of the dependent variable of the GLMM. We also derived the asymptotic properties of this test, and because power information does not exist for either our new test statistic or Liao's test, we implemented a power study to demonstrate the superiority of these tests over the alternatively proposed F test. Using an example, we show the application of the test and then discuss its possible restrictions with respect to the distribution of the random effects.

关键词： generalized linear mixed model group comparisons Wald statistic general linear hypothesis

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

model selection for prediction in generalized linear mixed model

Model selection for prediction in Generalized Linear Mixed M...

引用

作者： Qiong Wu Eindhoven University of Technology

学位级别：硕士

model selection is demanded in both high dimensional oversized dataset and low dimension com- plex framework. The traditional approach, i. e., stepwise regression which includes forward selec- tion and backward elimination is often used by the linear regression model and the generalized linear model. However, stepwise regression faces multiple test issue and it is not recommended. When we have repeat binary data, the outcomes within the same subject are correlated. Gener- alized linear mixed model (GLMM) is the suitable model to analyze this type of data. However, GLMM is restricted to few predictors, since the presence of many predictors leads to unstable estimates. Therefore, the model selection is needed. The problem in model selection for GLMM is much more complicated than generalized linear model since the selection on covariance matrix is not straightforward. Furthermore, model selections for GLMM are relatively new with the limited number of algorithms available in the literature and there are limited comparisons among those model selection methods either theoretically or through simulation. Therefore, the objective of this thesis is to systematically compare the currently available model selection methods for GLMM in the literature using simulation studies. After the investigation, five algorithms, namely, pen- alized quasi-likelihood (PQL) based Lasso, maximum likelihood (ML) based Lasso, PQL-based Boost, generalized estimating equation (GEE) based Boost and PQL-based joint selection (rpql) are selected. Only PQL-based rpql could perform both fixed effects and random effects selection, while the rest only perform fixed effects selection and the random effects need to be chosen manu- ally. Furthermore, PQL-based rqpl and GEE-based Boost are designed for low-dimensional data, and ML-based Lasso is for high-dimensional data. PQL-based Lasso and PQL-based Boost can be used for both low-dimensional and high-dimensional data. In order to compare the perform- ance

关键词： generalized linear mixed model model selection Lasso Boosting Maximum likelihood Penalized quasi-likelihood generalized estimating equation Prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：