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Showing 4 results for Maximum Likelihood Estimator
Zeynab Aghabazaz, Mohammad Hossein Alamatsaz,
Volume 17, Issue 2 (3-2013)
Abstract
Abstract: Depending on the type of distribution, estimation of parameters are not sometimes simple in practice. In particular, this is the case for Birnbaum-Saunders distribution (BS). In this article, we present four different methods for estimating the parameters of a BS distribution. First, a simple graphical technique, analogous to probability plotting, is used to estimate the parameters and check for goodness-of-fit of failure times following a Birnbaum-Saunders distribution. Then, the maximum likelihood estimators and a modification of the moment estimators of a two-parameter Birnbaum–Saunders distribution are discussed. Finally, The jackknife technique is considered as another method which is appropriate for the small sample size case. Monte Carlo simulation is also used to compare the performance of all these estimators.
Mehrangiz Falahati-Naeini,
Volume 19, Issue 1 (6-2014)
Abstract
In this article introduce the sequential order statistics. Therefore based on multiply Type-II censored sample of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two- parameter exponential distributions under the assumption that the prior distribution is given by an inverse gamma distribution and the Bayes estimator with respect to squared error loss is calculated. Moreover, prediction of future failure time is considered.
Finally in example Bayesian estimator and non-bayesian estimatores, namely the Best Linear Unbiased Estimator (BLUE) and Approximate Maximum Likelihood Estimator (AMLE) are derived.
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Volume 24, Issue 2 (3-2020)
Abstract
The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data.
In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this estimator in the face of a dataset which includes a number of outliers.
Taban Baghfalaki, Parvaneh Mehdizadeh, Mahdy Esmailian,
Volume 26, Issue 1 (12-2021)
Abstract
Joint models use in follow-up studies to investigate the relationship between longitudinal markers and survival outcomes
and have been generalized to multiple markers or competing risks data. Many statistical achievements in the field of joint
modeling focuse on shared random effects models which include characteristics of longitudinal markers as explanatory variables
in the survival model. A less-known approach is the joint latent class model, assuming that a latent class structure
fully captures the relationship between the longitudinal marker and the event risk. The latent class model may be appropriate
because of the flexibility in modeling the relationship between the longitudinal marker and the time of event, as well as the
ability to include explanatory variables, especially for predictive problems. In this paper, we provide an overview of the joint
latent class model and its generalizations. In this regard, first a review of the discussed models is introduced and then the
estimation of the model parameters is discussed. In the application section, two real data sets are analyzed.
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