检索结果-内蒙古大学图书馆

Censored autoregressive regression models with Student-t innovations

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2024年第3期52卷 804-828页

作者： Valeriano, Katherine A. L. Schumacher, Fernanda L. Galarza, Christian E. Matos, Larissa A. Escuela Super Politecn Litoral Dept Matemat Guayaquil 090112 Ecuador Ohio State Univ Coll Publ Hlth Div Biostat Columbus OH 43210 USA Univ Estadual Campinas Dept Estat BR-13083859 Campinas Brazil

Data collected over time are common in applications and may contain censored or missing observations, making it difficult to use standard statistical procedures. This article proposes an algorithm to estimate the parameters of a censored linear regression model with errors serially correlated and innovations following a Student-t distribution. This distribution is widely used in the statistical modelling of data containing outliers because its longer-than-normal tails provide a robust approach to handling such data. The maximum likelihood estimates of the proposed model are obtained through a stochastic approximation of the EM algorithm. The methods are applied to an environmental dataset regarding ammonia-nitrogen concentration, which is subject to a limit of detection (left censoring) and contains missing observations. Additionally, two simulation studies are conducted to examine the asymptotic properties of the estimates and the robustness of the model. The proposed algorithm and methods are implemented in the R package ARCensReg.

关键词： Autoregressive models censored data heavy-tailed distributions missing data saem algorithm

来源：评论

学校读者我要写书评

暂无评论

Estimation of parameters in incomplete data models defined by dynamical systems

引用

JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2007年第9期137卷 2815-2831页

作者： Donnet, Sophie Samson, Adeline INSERM U738 Paris France Univ Paris 11 Math Lab F-91400 Orsay France Univ Paris 07 UFR Med Paris France

Parametric incomplete data models defined by ordinary differential equations (ODEs) are widely used in biostatistics to describe biological processes accurately. Their parameters are estimated on approximate models, whose regression functions are evaluated by a numerical integration method. Accurate and efficient estimations of these parameters are critical issues. This paper proposes parameter estimation methods involving either a stochastic approximation EM algorithm (saem) in the maximum likelihood estimation, or a Gibbs sampler in the Bayesian approach. Both algorithms involve the simulation of non-observed data with conditional distributions using Hastings-Metropolis (H-M) algorithms. A modified H-M algorithm, including an original local linearization scheme to solve the ODEs, is proposed to reduce the computational time significantly. The convergence on the approximate model of all these algorithms is proved. The errors induced by the numerical solving method on the conditional distribution, the likelihood and the posterior distribution are bounded. The Bayesian and maximum likelihood estimation methods are illustrated on a simulated pharmacokinetic nonlinear mixed-effects model defined by an ODE. Simulation results illustrate the ability of these algorithms to provide accurate estimates. (c) 2007 Elsevier B.V. All rights reserved.

关键词： Bayesian estimation incomplete data model local linearization scheme MCMC algorithm nonlinear mixed-effects model ODE integration saem algorithm

来源：评论

学校读者我要写书评

暂无评论

Geostatistical estimation and prediction for censored responses

引用

SPATIAL STATISTICS 2018年 23卷 109-123页

作者： Ordonez, Jose A. Bandyopadhyay, Dipankar Lachos, Victor H. Cabral, Celso R. B. Univ Estadual Campinas Dept Stat Campinas SP Brazil Virginia Commonwealth Univ Dept Biostat Richmond VA USA Univ Connecticut Dept Stat 215 Glenbrook Rd U-4120 Storrs CT 06269 USA Univ Fed Amazonas Dept Stat Manaus Amazonas Brazil

Spatially-referenced geostatistical responses that are collected in environmental sciences research are often subject to detection limits, where the measures are not fully quantifiable. This leads to censoring (left, right, interval, etc.), and various ad hoc statistical methods (such as choosing arbitrary detection limits, or data augmentation) are routinely employed during subsequent statistical analysis for inference and prediction. However, inference may be imprecise and sensitive to the assumptions and approximations involved in those arbitrary choices. To circumvent this, we propose an exact maximum likelihood estimation framework of the fixed effects and variance components and related prediction via a novel application of the Stochastic Approximation of the Expectation Maximization (saem) algorithm, allowing for easy and elegant estimation of model parameters under censoring. Both simulation studies and application to a real dataset on arsenic concentration collected by the Michigan Department of Environmental Quality demonstrate the advantages of our method over the available naive techniques in terms of finite sample properties of the estimates, prediction, and robustness. The proposed methods can be implemented using the R package CensSpatial. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Censored geostatistical data Kriging Limit of detection (LOD) saem algorithm

来源：评论

学校读者我要写书评

暂无评论

Detecting anomalies in fibre systems using 3-dimensional image data

引用

STATISTICS AND COMPUTING 2020年第4期30卷 817-837页

作者： Dresvyanskiy, Denis Karaseva, Tatiana Makogin, Vitalii Mitrofanov, Sergei Redenbach, Claudia Spodarev, Evgeny Reshetnev Siberian State Univ Sci & Technol 31 Krasnoyarsky Rabochy Ave Krasnoyarsk 660037 Russia Univ Ulm Inst Stochast D-89069 Ulm Germany Tech Univ Kaiserslautern Fachbereich Math Postfach 3049 D-67653 Kaiserslautern Germany

We consider the problem of detecting anomalies in the directional distribution of fibre materials observed in 3D images. We divide the image into a set of scanning windows and classify them into two clusters: homogeneous material and anomaly. Based on a sample of estimated local fibre directions, for each scanning window we compute several classification attributes, namely the coordinate wise means of local fibre directions, the entropy of the directional distribution, and a combination of them. We also propose a new spatial modification of the Stochastic Approximation Expectation-Maximization (saem) algorithm. Besides the clustering we also consider testing the significance of anomalies. To this end, we apply a change point technique for random fields and derive the exact inequalities for tail probabilities of a test statistic. The proposed methodology is first validated on simulated images. Finally, it is applied to a 3D image of a fibre reinforced polymer.

关键词： Anomaly detection Classification Fibre composite Directional distribution Change point problem Entropy saem algorithm

来源：评论

学校读者我要写书评

暂无评论

LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation

引用

STATISTICS AND COMPUTING 2014年第3期24卷 443-460页

作者： Arribas-Gil, Ana Bertin, Karine Meza, Cristian Rivoirard, Vincent Univ Carlos III Madrid Dept Estadist E-28903 Getafe Spain Univ Valparaiso CIMFAV Fac Ingn Valparaiso Chile Univ Paris 09 CEREMADE CNRS UMR 7534 F-75775 Paris France INRIA Paris Rocquencourt Class Team Paris France

Parametric nonlinear mixed effects models (NLMEs) are now widely used in biometrical studies, especially in pharmacokinetics research and HIV dynamics models, due to, among other aspects, the computational advances achieved during the last years. However, this kind of models may not be flexible enough for complex longitudinal data analysis. Semiparametric NLMEs (SNMMs) have been proposed as an extension of NLMEs. These models are a good compromise and retain nice features of both parametric and nonparametric models resulting in more flexible models than standard parametric NLMEs. However, SNMMs are complex models for which estimation still remains a challenge. Previous estimation procedures are based on a combination of log-likelihood approximation methods for parametric estimation and smoothing splines techniques for nonparametric estimation. In this work, we propose new estimation strategies in SNMMs. On the one hand, we use the Stochastic Approximation version of EM algorithm (saem) to obtain exact ML and REML estimates of the fixed effects and variance components. On the other hand, we propose a LASSO-type method to estimate the unknown nonlinear function. We derive oracle inequalities for this nonparametric estimator. We combine the two approaches in a general estimation procedure that we illustrate with simulations and through the analysis of a real data set of price evolution in on-line auctions.

关键词： LASSO Nonlinear mixed-effects model On-line auction saem algorithm Semiparametric estimation

来源：评论

学校读者我要写书评

暂无评论

Censored regression models with autoregressive errors: A likelihood-based perspective

引用

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2017年第4期45卷 375-392页

作者： Schumacher, Fernanda L. Lachos, Victor H. Dey, Dipak K. IBGE Diretoria Pesquisas Rio De Janeiro Brazil Univ Estadual Campinas Dept Estat BR-13083859 Campinas SP Brazil Univ Connecticut Dept Stat Storrs CT 06269 USA

In many studies that involve time series variables limited or censored data are naturally collected. Practitioners commonly disregard censored data cases or replace these observations with some function of the limit of detection, which often results in biased estimates. In this article we propose an analytically tractable and efficient stochastic approximation of the EM (saem) algorithm to obtain the maximum likelihood estimates of the parameters of censored regression models with autoregressive errors of order, say, p. This approach permits easy and fast estimation of the parameters of autoregressive models when censoring is present and as a byproduct, enables predictions of unobservable values of the response variable. We use simulations to investigate the asymptotic properties of the saem estimates and prediction accuracy. Finally the method is illustrated using two time series data sets, where the measurements are subject to the detection limit of the recording devices. The proposed algorithm and methods are implemented in the new R package ARCensReg. The Canadian Journal of Statistics 45: 375-392;2017 (c) 2017 Statistical Society of Canada

关键词： Autoregressive AR(p) models censored data limit of detection saem algorithm MSC 2010: Primary 62M10 secondary 62N01

来源：评论

学校读者我要写书评

暂无评论

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

引用

JOURNAL OF AGRICULTURAL SCIENCE 2018年第10期156卷 1216-1221页

作者： Santos, H. B. Vieira, D. A. Souza, L. P. Santos, A. L. Santos, F. R. Araujo Neto, F. R. Inst Fed Goiano Campus Rio VerdeRodovia Sul GoianaKm 01 BR-75901970 Rio Verde Go Brazil Univ Fed Mato Grosso Campus Rondonopolis BR-78735901 Rondonopolis Mato Grosso Brazil

The objective of the current paper was to apply mixed models to adjust the growth curve of quail lines for meat and laying hens and present the rates of instantaneous, relative and absolute growth. A database was used with birth weight records up to the 148th day of female quail of the lines for meat and posture. The models evaluated were Brody, Von Bertalanffy, Logistic and Gompertz and the types of residues were constant, combined, proportional and exponential. The Gompertz model with the combined residue presented the best fit. Both strains present a high correlation between the parameters asymptotic weight (A) and average growth rate (k). The two strains presented a different growth profile. However, growth rates allow greater discernment of growth profiles. The meat line presented a higher growth rate (6.95 g/day) than the lineage for laying (3.65 g/day). The relative growth rate was higher for lineage for laying (0.15%) in relation to the lineage for meat (0.13%). The inflection point of both lines is on the first third of the growth curve (up to 15 days). All results suggest that changes in management or nutrition could optimize quail production.

关键词： Coturnix coturnix coturnix Coturnix coturnix japonica model selection saem algorithm

来源：评论

学校读者我要写书评

暂无评论

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 247卷 108095-108095页

作者： Lavalley-Morelle, Alexandra Mentre, France Comets, Emmanuelle Mullaert, Jimmy Univ Paris Cite INSERM IAME F-75018 Paris France Bichat Claude Bernard Univ Hosp Dept Epidemiol Biostat & Clin Res AP HP F-75018 Paris France Univ Rennes Inserm EHESP IrsetUMRS 1085 F-35000 Rennes France Univ Paris Saclay Inst Curie Canc & Genome UVSQ F-92210 St Cloud France

Background and Objective: Joint modeling of longitudinal and time -to -event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time -toevent models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre -implemented functions (exponential, Weibull, ...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. Methods: We used the saemix package, designed to fit nonlinear mixed -effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (saem) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed -effects model for the longitudinal submodel, with a single terminal event or a competing risk model. Results: For all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM),

关键词： Competing risks Joint modeling Mixed-effects model saem algorithm saemix Time-to-event

来源：评论

学校读者我要写书评

暂无评论

Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics

引用

JOURNAL OF MATHEMATICAL BIOLOGY 2022年第4期85卷 40-40页

作者： Narci, Romain Delattre, Maud Laredo, Catherine Vergu, Elisabeta Univ Paris Saclay INRAE MaIAGE F-78350 Jouy En Josas France

The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the saem algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.

关键词： Kalman filter Latent variables Parametric inference Random effects saem algorithm Stochastic compartmental models

来源：评论

学校读者我要写书评

暂无评论

Likelihood-based inference for spatiotemporal data with censored and missing responses

引用

ENVIRONMETRICS 2021年第3期32卷 e2663-e2663页

作者： Valeriano, Katherine A. L. Lachos, Victor H. Prates, Marcos O. Matos, Larissa A. Univ Estadual Campinas Dept Stat Sao Paulo SP Brazil Univ Connecticut Dept Stat Storrs CT 06269 USA Univ Fed Minas Gerais Dept Stat Belo Horizonte MG Brazil

This paper proposes an alternative method to deal with spatiotemporal data with censored and missing responses using the saem algorithm. This algorithm is a stochastic approximation of the widely used EM algorithm and is an important tool for models in which the E-step does not have an analytic form. Besides the algorithm developed to estimate the model parameters from a likelihood-based perspective, we present analytical expressions to compute the observed information matrix. Global influence measures are also developed and presented. Several simulation studies are conducted to examine the asymptotic properties of the saem estimates. The proposed method is illustrated by environmental data analysis. The computing codes are implemented in the new R package StempCens.

关键词： limit of detection missing data observed information matrix saem algorithm spatiotemporal data

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：