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Showing 9 results for Maximum Likelihood Estimator
Ghobad Barmalzan, Abdolreza Sayyareh,
Volume 4, Issue 2 (3-2011)
Abstract
Suppose we have a random sample of size n of a population with true density h(.). In general, h(.) is unknown and we use the model f as an approximation of this density function. We do inference based on f. Clearly, f must be close to the true density h, to reach a valid inference about the population. The suggestion of an absolute model based on a few obsevations, as an approximation or estimation of the true density, h, results a great risk in the model selection. For this reason, we choose k non-nested models and investigate the model which is closer to the true density. In this paper, we investigate this main question in the model selection that how is it possible to gain a collection of appropriate models for the estimation of the true density function h, based on Kullback-Leibler risk.
Hamazeh Torabi, Narges Montazeri, Fatemeh Ghasemian,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper, some various families constructed from the logit of the generalized Beta, Beta, Kumar, generalized Gamma, Gamma, Weibull, log gamma and Logistic distributions are reviewed. Then a general family of distributions generated from the logit of the normal distribution is proposed. A special case of this family, Normal-Uniform distribution, is defined and studied. Various properties of the distribution are also explored. The maximum likelihood and minimum spacings estimators of the parameters of this distribution are obtained. Finally, the new distribution is effectively used to analysis a real survival data set.
Akbar Asgharzadeh, Mina Azizpour, Reza Valiollahi,
Volume 9, Issue 1 (9-2015)
Abstract
One of the drawbacks of the type II progressive censoring scheme is that the length of the experiment can be very large. Because of that, recently a new censoring scheme named as the type II progressively hybrid censored scheme has received considerable interest among the statisticians. In this paper, the statistical inference for the half-logistic distribution is discussed based on the progressively type II hybrid censored samples. The maximum likelihood estimator, the approximate maximum likelihood estimator and the Bayes estimator of parameter using Lindley approximation and MCMC method are obtained. Asymptotic confidence intervals, Bootstrap confidence intervals and Bayesian credible intervals are obtained. Different point and interval estimators are compared using Monte Carlo simulation. A real data set is presented for illustrative purposes.
Eisa Mahmoudi, Somayeh Abolhosseini,
Volume 10, Issue 1 (8-2016)
Abstract
In this paper we propose a new two-parameters distribution, which is an extension of the Lindley distribution with increasing and bathtub-shaped failure rate, called as the Lindley-logarithmic (LL) distribution. The new distribution is obtained by compounding Lindley (L) and Logarithmic distributions. We obtain several properties of the new distribution such as its probability density function, its failure rate functions, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented in this paper. At the end, in order to show the flexibility and potentiality of this new class, some series of real data is used to fit.
Jafar Ahmadi, Mansoureh Razmkhah,
Volume 11, Issue 1 (9-2017)
Abstract
Consider a repairable system which starts operating at t=0. Once the system fails, it is immediately replaced by another one of the same type or it is repaired and back to its working functions. In this paper, the system's activity is studied from t>0 for a fixed period of time w. Different replacement policies are considered. In each cases, for a fixed period of time w, the probability model and likelihood function of repair process, say window censored, are obtained. The obtained results depend on the lifetime distribution of the original system, so, expression for the maximum likelihood estimator and Fisher information are derived, by assuming the lifetime follows an exponential distribution.
Mohamad Bayat, Hamzeh Torabi,
Volume 12, Issue 1 (9-2018)
Abstract
Nowadays, the use of various censorship methods has become widespread in industrial and clinical tests. Type I and Type II progressive censoring are two types of these censors. The use of these censors also has some disadvantages. This article tries to reduce the defects of the type I progressive censoring by making some change to progressive censorship. Considering the number and the time of the withdrawals as a random variable, this is done. First, Type I, Type II progressive censoring and two of their generalizations are introduced. Then, we introduce the new censoring based on the Type I progressive censoring and its probability density function. Also, some of its special cases will be explained and a few related theorems are brought. Finally, the simulation algorithm is brought and for comparison of introduced censorship against the traditional censorships a simulation study was done.
Mahdieh Mozafari, Mehrdad Naderi, Alireza Arabpour,
Volume 12, Issue 1 (9-2018)
Abstract
This paper introduces a new distribution based on extreme value distribution. Some properties and characteristics of the new distribution such as distribution function, moment generating function and skewness and kurtosis are studied. Finally, by computing the maximum likelihood estimators of the new distribution's parameters, the performance of the model is illustrated via two real examples.
Mahdi Teimouri,
Volume 14, Issue 1 (8-2020)
Abstract
The class of α-stable distributions incorporates both heavy tails and skewness and so are the most widely used class of distributions in several fields of study which incorporates both the skewness and heavy tails. Unfortunately, there is no closed-form expression for the density function of almost all of the members of this class, and so finding the maximum likelihood estimator for the parameters of this distribution is a challenging problem. In this paper, in order to tackle this issue, we propose some type of EM algorithm. The performance of the proposed EM algorithm is demonstrated via simulation and analyzing three sets of real data.
, Dr Seyed Kamran Ghoreishi,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, we first introduce semi-parametric heteroscedastic hierarchical models. Then, we define a new version of the empirical likelihood function (Restricted Joint Empirical likelihood) and use it to obtain the shrinkage estimators of the models' parameters in these models. Under different assumptions, a simulation study investigates the better performance of the restricted joint empirical likelihood function in the analysis of semi-parametric heterogeneity hierarchical models. Furthermore, we analyze an actual data set using the RJEL method.
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