Standard Error of the Method of Simulated Moment Estimator for Generalized Linear Mixed Models
نویسنده
چکیده
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. This article considers standard error estimation of the method of simulated moment estimator for generalized linear mixed models. In literature, parametric bootstrap is used to estimate the covariance matrix, in which we use the estimator to generate simulated moments. To avoid the bias introduced by estimating the parameter and to deal with the correlated observations, we propose a two-stage block nonparametric bootstrap to estimate the standard errors. It is shown from simulation study that the proposed method performs well.
منابع مشابه
Stochastic Restricted Two-Parameter Estimator in Linear Mixed Measurement Error Models
In this study, the stochastic restricted and unrestricted two-parameter estimators of fixed and random effects are investigated in the linear mixed measurement error models. For this purpose, the asymptotic properties and then the comparisons under the criterion of mean squared error matrix (MSEM) are derived. Furthermore, the proposed methods are used for estimating the biasing parameters. Fin...
متن کاملA New Ridge Estimator in Linear Measurement Error Model with Stochastic Linear Restrictions
In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...
متن کاملمقایسه مدلهای آمیخته خطی تعمیمیافته و مدلهای خطی تعمیمیافته در تعیین عوامل مرتبط با بیماری دیابت نوع2 در استان یزد
. Comparison of Generalized Linear Mixed and Generalized Linear Models in Determining Type II Diabetes Related Factors in Yazd Fallahzadeh H(Ph.D)1,Rahmanian M(Ph.D)2,Emadi M(Ph.D)3,Asadi F(M.Sc)4 1. Professor of Biostatistics, Department of Biostatistics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 2. Corresponding Author: Graduate student of Biostat...
متن کاملInfluence Measures in Ridge Linear Measurement Error Models
Usually the existence of influential observations is complicated by the presence of collinearity in linear measurement error models. However no method of influence measure available for the possible effect's that collinearity can have on the influence of an observation in such models. In this paper, a new type of ridge estimator based corrected likelihood function (REC) for linear measurement e...
متن کاملTruncated Linear Minimax Estimator of a Power of the Scale Parameter in a Lower- Bounded Parameter Space
Minimax estimation problems with restricted parameter space reached increasing interest within the last two decades Some authors derived minimax and admissible estimators of bounded parameters under squared error loss and scale invariant squared error loss In some truncated estimation problems the most natural estimator to be considered is the truncated version of a classic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Communications in Statistics - Simulation and Computation
دوره 42 شماره
صفحات -
تاریخ انتشار 2013