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Showing 4 results for Karami
N Abassi, R Alijani, Karami, Hosseini,
Volume 15, Issue 2 (3-2011)
Abstract
As in recent years the scientific productivity about ISI database and other related database have been increased, it is eligible for researchers of Statistics in Iran to know more about these journals and their statues in ISI database. In this study with the use of bibliometric methods, we have reviewed the status of Statistics and Probability . From all nations around the world, these are only 12 countries whitch are active in publishing these 80 journals. Finding also show that England and USA are the most active countries in publishing Statistics journals. Each of these two countries publish 24 journals and both stands at the first rank in this regard. We also found that out of 80 Statistics journals in ISI database, 71 titles are published in English language and only 9 journals are published in other languages.
Miss Atefeh Karami,
Volume 23, Issue 1 (9-2018)
Abstract
The normal distribution plays an important role in statistical analysis. However, a researcher may also wish to construct another symmetric distributions which fit the data better than the normal distribution. For this purpose, more flexible distributions have been introduced. In this thesis, we introduce some of such distributions. We first introduce the slash distribution as a family of mixed-normal scale distributions. The slash distribution can be used instead of normal distribution, in many situations. We also introduce Skew-slash. Then, a new modified-slash distribution is discussed. We also study a modified Skew-slash distribution. Some properties of the distributions discussed in this thesis are given. In particular, we present the stochastic representations, density functions and moments of the distributions.
Miss Tayebeh Karami, Dr Muhyiddin Izadi, Dr Mehrdad Niaparast,
Volume 26, Issue 1 (12-2021)
Abstract
The subject of classification is one of the important issues in different sciences. Logistic regression is one of the statistical
methods to classify data in which the underlying distribution of the data is assumed to be known. Today, researchers in
addition to statistical methods use other methods such as machine learning in which the distribution of the data does not
need to be known. In this paper, in addition to the logistic regression, some machine learning methods including CART
decision tree, random forest, Bagging and Boosting of supervising learning are introduced. Finally, using four real data
sets, we compare the performance of these algorithms with respect to the accuracy measure.
Dr. Akram Kohansal, Mrs. Atefeh Karami,
Volume 28, Issue 1 (9-2023)
Abstract
The statistical inference of the multi-component stress-strength parameter, $R_{s,k}$, is considered in the three-parameter Weibull distribution. The problem is studied in two cases. In the first case, assuming that the stress and strength variables have common shape and location parameters and non-common scale parameters and all these parameters are unknown, the maximum likelihood estimation and the Bayesian estimation of the parameter $R_{s,k}$ are investigated. In this case, as the Bayesian estimation does not have a closed form, it is approximated by two methods, Lindley and $mbox{MCMC}$. Also, asymptotic confidence intervals have been obtained. In the second case, assuming that the stress and strength variables have known common shape and location parameters and non-common and unknown scale parameters, the maximum likelihood estimation, the uniformly minimum variance unbiased estimators, the exact Bayesian estimation of the parameter $R_{s,k}$ and the asymptotic confidence interval is calculated. Finally, using Monte Carlo simulation, the performance of different estimators has been compared.
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