A General Mixed Linear Model with Left-Censored Data
نویسندگان
چکیده
منابع مشابه
A linear mixed-effects model for multivariate censored data.
We apply a linear mixed-effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley-James method in an iterated Monte Carlo expectation-maximization algorithm, wherein the Monte Carlo E-step is implemented using the Metropolis-Hastings algorithm. From simulation studies, this approach compares favorably with the marginal independence approach, ...
متن کاملEstimation and Reconstruction Based on Left Censored Data from Pareto Model
In this paper, based on a left censored data from the twoparameter Pareto distribution, maximum likelihood and Bayes estimators for the two unknown parameters are obtained. The problem of reconstruction of the past failure times, either point or interval, in the left-censored set-up, is also considered from Bayesian and non-Bayesian approaches. Two numerical examples and a Monte Carlo simulatio...
متن کاملInfluence Diagnostics for the Normal Linear Model with Censored Data
Methods of detecting influential observations for the normal model for censored data are proposed. These methods include one-step deletion methods, deletion of observations and the empirical influence function. Emphasis is placed on assessing the impact that a single ohservatioii has on the estimation of coefficients of the model. Functions of tlie coeflkieiits such as tlie median lifetime are ...
متن کاملLinear Regression with Interval Censored Data
Ž . are observed instead of the usual Y , Z , where I for some censori i i Y T 4 i i ing times T . This observation scheme is also called the binary choice model i Ž . Coslett 1987 and Case 1 interval censoring Groeneboom and Wellner Ž . 4 4 1992 . We assume that the errors are independent of Z , T and are i i i Ž . i.i.d. variables with a common distribution F t . Situations involving interval...
متن کاملKernel Ridge Estimator for the Partially Linear Model under Right-Censored Data
Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2008
ISSN: 2287-7843
DOI: 10.5351/ckss.2008.15.6.969