检索结果-内蒙古大学图书馆

Monitoring two-stage processes with binomial data using generalized linear model-based control charts

QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2016年第3期13卷 241-262页

作者： Amiri, Amirhossein Yeh, Arthur B. Asgari, Ali Shahed Univ Dept Ind Engn Tehran Iran Bowling Green State Univ Dept Appl Stat & Operat Res Bowling Green OH 43403 USA

In this study, we propose a control chart for monitoring two-stage processes whose quality characteristic to be monitored in the second stage follows a binomial distribution. The proposed control chart is based on the deviance residual in which essentially the generalized log-likelihood ratio statistic is obtained from the generalized linear model. To establish the relationship between the first- and second-stage quality characteristics, we propose using a new link function in a generalized linear model framework. The performance of the proposed control chart with the new link function is compared with that under the traditional logit link function in terms of the average run length criterion. In addition, the performance of the proposed control chart is compared with the chart designed based on the original residuals under the new link function as well as the traditional np-chart applied for monitoring the binomial quality characteristic in the second stage.

关键词： Average run length binomial data deviance residual generalized linear model two-stage process

来源：评论

学校读者我要写书评

暂无评论

Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a generalized linear model

引用

INTERNATIONAL JOURNAL OF CLIMATOLOGY 2014年第14期34卷 3654-3670页

作者： Beecham, Simon Rashid, Mamunur Chowdhury, Rezaul K. Univ S Australia Sch Nat & Built Environm Mawson Lakes SA 5095 Australia Univ S Australia Sch Nat & Built Environm Ctr Water Management & Reuse Mawson Lakes SA 5095 Australia United Arab Emirates Univ Dept Civil & Environm Engn Al Ain U Arab Emirates

The intention of this study was to identify a suitable generalized linear model (GLM) for modelling multi-site daily rainfall in the Onkaparinga catchment in South Australia and to examine the suitability of the model for downscaling of General Circulation model (GCM) rainfall projections. A GLM was applied and multi-site daily rainfall was downscaled using National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets. Nineteen large-scale atmospheric and circulation variables were selected at first and these were eventually reduced, based on correlation with daily rainfall, to 10 final variables to be used in the model. First, logistic regression was used to identify the wet and dry days, then wet day rainfall was modelled using a gamma distribution. The model was fitted for a calibration period (1991-2010) and it was then validated over the period 1981-1990. Several summary statistics including mean, standard deviation, number of wet days, maximum rainfall amount and lag 1 and lag 2 autocorrelations were used to check the model performance. The 2.5th and 97.5th percentiles of the simulated rainfall statistics were plotted against the observed rainfall statistics and it was shown that most of the observed statistics were within these bounds. Area averaged and station wise monthly, seasonal and annual totals for observed and simulated rainfall were estimated and compared. The overall performance of the GLM to downscale rainfall was considered satisfactory. However, a few discrepancies were observed in different performance statistics. Parameterization of the model to capture the local convective variability of rainfall would increase the model performance. It was found overall that the GLM can be applied for downscaling of GCM rainfall projections for this catchment.

关键词： generalized linear model rainfall modelling statistical downscaling climate change General Circulation model spatial and temporal variability

来源：评论

学校读者我要写书评

暂无评论

Quantifying the interactions among metal mixtures in toxicodynamic process with generalized linear model

引用

JOURNAL OF HAZARDOUS MATERIALS 2018年 345卷 97-106页

作者： Feng, Jianfeng Gao, Yongfei Ji, Yijun Zhu, Lin Nankai Univ Key Lab Pollut Proc & Environm Criteria Minist Educ Coll Environm Sci & Engn Tianjin 300071 Peoples R China Nankai Univ Tianjin Key Lab Environm Remediat & Pollut Contro Coll Environm Sci & Engn Tianjin 300071 Peoples R China Chinese Acad Agr Sci Inst Grassland Res Hohhot 010010 Peoples R China

Predicting the toxicity of chemical mixtures is difficult because of the additive, antagonistic, or synergistic interactions among the mixture components. Antagonistic and synergistic interactions are dominant in metal mixtures, and their distributions may correlate with exposure concentrations. However, whether the interaction types of metal mixtures change at different time points during toxicodynamic (TD) processes is undetermined because of insufficient appropriate models and metal bioaccumulation data at different time points. In the present study, the generalized linear model (GLM) was used to illustrate the combined toxicities of binary metal mixtures, such as Cu-Zn, Cu-Cd, and Cd-Pb, to zebrafish larvae (Danio rerio). GLM was also used to identify possible interaction types among these method for the traditional concentration addition (CA) and independent action (IA) models. Then the GLM were applied to quantify the different possible interaction types for metal mixture toxicity (Cu-Zn, Cu-Cd, and Cd-Pb to D. rerio and Ni-Co to Oligochaeta Enchytraeus crypticus) during the TD process at different exposure times. We found different metal interaction responses in the TD process and interactive coefficients significantly changed at different exposure times (p<0.05), which indicated that the interaction types among Cu-Zn, Cu-Cd, Cd-Pb and Ni-Co were time dependent. Our analysis highlighted the importance of considering joint actions in the TD process to understand and predict metal mixture toxicology on organisms. Moreover, care should be taken when evaluating interactions in toxicity prediction because results may vary at different time points. The GLM could be an alternative or complementary approach for BLM to analyze and predict metal mixture toxicity. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Toxicodynamic generalized linear model Interaction types Toxicity Metal mixtures

来源：评论

学校读者我要写书评

暂无评论

Variable Selection for generalized linear model with Highly Correlated Covariates

引用

Acta Mathematica Sinica,English Series 2024年第6期40卷 1458-1480页

作者： Li Li YUE Wei Tao WANG Gao Rong LI School of Statistics and Data Science Nanjing Audit UniversityNanjing 211815P.R.China School of Mathematics Statistics and MechanicsBeijing University of TechnologyBeijing 100124P.R.China School of Statistics Beijing Normal UniversityBeijing 100875P.R.China

The penalized variable selection methods are often used to select the relevant covariates and estimate the unknown regression coefficients simultaneously,but these existing methods may fail to be consistent for the setting with highly correlated *** this paper,the semi-standard partial covariance(SPAC)method with Lasso penalty is proposed to study the generalized linear model with highly correlated covariates,and the consistencies of the estimation and variable selection are shown in high-dimensional settings under some regularity *** simulation studies and an analysis of colon tumor dataset are carried out to show that the proposed method performs better in addressing highly correlated problem than the traditional penalized variable selection methods.

关键词： generalized linear model highly correlated covariates Lasso penalty semi-standard partial covariance variable selection

来源：评论

学校读者我要写书评

暂无评论

A new method for choosing the biasing parameter in ridge estimator for generalized linear model

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2018年 183卷 96-101页

作者： Algamal, Zakariya Yahya Univ Mosul Dept Stat & Informat Mosul Iraq

Multicollinearity problem arises frequently in several modern applications, such as chemometrics, biology, and other scientific fields. The common feature of the multicollinearity problem is that a large number of predictors are highly correlated. generalized linear model is a powerful and a popular approach for modeling a large variety of regression data. It is well known that the existence of multicollinearity can inflate the variance of the maximum likelihood estimator. To reduce the effects of multicollinearity, the ridge estimator has been efficiently demonstrated to be an attractive method. However, the choice of the biasing parameter of the ridge estimator is critical. Our aim is to efficiently estimate such a biasing parameter. Towards this aim, a kidney-inspired algorithm, which is a population-based algorithm inspiring by the kidney process in the human body, is proposed. Extensive comparisons with different classical biasing parameter estimating methods are conducted through simulation and real data application. The results demonstrate that our proposed approach is able to find the best biasing parameter value with high prediction accuracy. Further, the results indicate that the performance of our proposed approach is superior to that of other competitor methods.

关键词： Multicollinearity generalized linear model Ridge estimator Kidney-inspired algorithm

来源：评论

学校读者我要写书评

暂无评论

A generalized linear-model WITH NESTED STRATA OF EXTRA-POISSON VARIATION

引用

BIOMETRIKA 1987年第2期74卷 247-257页

作者： MORTON, R CSIRO DIV MATH & STAT CANBERRA ACT 2601 AUSTRALIA

We consider a scaled Poisson generalized linear model with random multiplicative errors associated with each stratum of a nested block structure. The normal equations are obtained. The quasi-likelihood is shown to be the product of a sequence of negative binomial quasi-likelihoods using weighted totals and correcting for margins. This is achieved by extending the definition of the quasi-likelihood which now includes a vector of weights. The usual asymptotic results still hold. Data on trap catches of insects provide a numerical example.

关键词： Binomials Statistical variance generalized linear model Analysis of variance Analytical estimating Insect traps Ecological modeling Covariance matrices Weighted averages

来源：评论

学校读者我要写书评

暂无评论

Going beyond oracle property: Selection consistency and uniqueness of local solution of the generalized linear model

引用

STATISTICAL METHODOLOGY 2016年第0期32卷 147-160页

作者： Ng, Chi Tim Oh, Seungyoung Lee, Youngjo Chonnam Natl Univ Dept Stat Kwangju 500757 South Korea Seoul Natl Univ Dept Stat Seoul 151747 South Korea

Recently, the selection consistency of penalized least square estimators has received a great deal of attention. For the penalized likelihood estimation with certain non-convex penalties, search space can be constructed within which there exists a unique local minimizer that exhibits selection consistency in high-dimensional generalized linear models under certain conditions. In particular, we prove that the SCAD penalty of Fan and Li (2001) and a new modified version of the unbounded penalty of Lee and Oh (2014) can be employed to achieve such a property. These results hold even for the non-sparse cases where the number of relevant covariates increases with the sample size. Simulation studies are provided to compare the performance of SCAD penalty and the newly proposed penalty. (C) 2016 Elsevier B.V. All rights reserved.

关键词： generalized linear model Penalized likelihood estimation Oracle property SCAD penalty Selection consistency

来源：评论

学校读者我要写书评

暂无评论

The negative binomial-Lindley generalized linear model: Characteristics and application using crash data

引用

ACCIDENT ANALYSIS AND PREVENTION 2012年 45卷 258-265页

作者： Geedipally, Srinivas Reddy Lord, Dominique Dhavala, Soma Sekhar Texas A&M Univ Texas Transportat Inst College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

There has been a considerable amount of work devoted by transportation safety analysts to the development and application of new and innovative models for analyzing crash data. One important characteristic about crash data that has been documented in the literature is related to datasets that contained a large amount of zeros and a long or heavy tail (which creates highly dispersed data). For such datasets, the number of sites where no crash is observed is so large that traditional distributions and regression models, such as the Poisson and Poisson-gamma or negative binomial (NB) models cannot be used efficiently. To overcome this problem, the NB-Lindley (NB-L) distribution has recently been introduced for analyzing count data that are characterized by excess zeros. The objective of this paper is to document the application of a NB generalized linear model with Lindley mixed effects (NB-L GLM) for analyzing traffic crash data. The study objective was accomplished using simulated and observed datasets. The simulated dataset was used to show the general performance of the model. The model was then applied to two datasets based on observed data. One of the dataset was characterized by a large amount of zeros. The NB-L GLM was compared with the NB and zero-inflated models. Overall, the research study shows that the NB-L GLM not only offers superior performance over the NB and zero-inflated models when datasets are characterized by a large number of zeros and a long tail, but also when the crash dataset is highly dispersed. Published by Elsevier Ltd.

关键词： Poisson-gamma Negative binomial Lindley generalized linear model Crash data

来源：评论

学校读者我要写书评

暂无评论

Disclosure risk reduction for generalized linear model output in a remote analysis system

引用

DATA & KNOWLEDGE ENGINEERING 2017年 111卷 90-102页

作者： Khan, Atikur R. O'Keefe, Christine M. CSIRO GPO Box 1700 Canberra ACT 2601 Australia

Remote analysis systems allow analysts to obtain statistical results without providing direct access to confidential data stored in a secure server system. An attacking analyst could send queries to a remote server to obtain outputs of statistical analyses and use those outputs for a disclosure attack. Statistical disclosure control (SDC) methods are used to modify remote analysis system (RAS) outputs in the protection of confidential information. Confidentiality protection through perturbation is one of the most commonly adopted SDC methods. In the case of generalized linear modelling, random noise is added to the estimated coefficients or to the associated estimating equation prior to getting estimates. This inflates the variances of estimators, and some efficiency and utility of estimators are lost. Thus the application of any perturbation based SDC method could result in an inefficient estimator, with the danger of producing worthless inferences. To date, little attention has been given to systematically controlling the disclosure risk and utility in SDC methods for RAS. In this paper, we develop a framework for the perturbation of estimating equations that enables an RAS to release modified generalized linear model output in such a way that the disclosure risk is not only reduced but also a good utility is maintained. Finally, we present some empirical results demonstrating the application of our framework for obtaining estimates from perturbed estimating equations of binary and count response models.

关键词： Confidentiality protection Data utility Efficiency loss generalized linear model Perturbation Remote analysis

来源：评论

学校读者我要写书评

暂无评论

Homogeneity detection for the high-dimensional generalized linear model

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2017年 114卷 61-74页

作者： Jeon, Jong-June Kwon, Sunghoon Choi, Hosik Univ Seoul Dept Stat Seoul 02504 South Korea Konkuk Univ Dept Appl Stat Seoul 05029 South Korea Kyonggi Univ Dept Appl Stat Suwon 16227 South Korea

We propose to use a penalized estimator for detecting homogeneity of the high dimensional generalized linear model. Here, the homogeneity is a specific model structure where regression coefficients are grouped having exactly the same value in each group. The proposed estimator achieves weak oracle property under mild regularity conditions and is invariant to the choice of reference levels when there are categorical covariates in the model. An efficient algorithm is also provided. Various numerical studies confirm that the proposed penalized estimator gives better performance than other conventional variable selection estimators when the model has homogeneity. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Categorical covariates generalized linear model Grouping penalty Oracle property

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：