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Showing 7 results for Measure
S Mahmoudi, F Golastaneh,
Volume 14, Issue 2 (3-2010)
Abstract
Mis Marzieh Baghban,
Volume 19, Issue 2 (2-2015)
Abstract
In reliability theory, some measures are introduced , called importance measures, to evaluate the relative importance
of individual components or groups of components in a system. Importance measures are quantitive criteria
that ranke the components according to their importance. In the literature, different importance measures are presented
based on different scenarios. These measures can be determined based on the system structure, reliability of
the components and/or component liftime distributions. The purpose of this paper is the study different importance
measures of the components of a system in reliability theory.
Majid Abiar, Abdolrahim Badamchizadeh,
Volume 22, Issue 1 (12-2017)
Abstract
In this paper, an M/M/1 queue with instantaneous Bernoulli feedback is studied in the event of server failure, the catastrophe occurs and after repair, it starts to work again. The transient response for the probability function of the system size is presented. The steady state analysis of system size probabilities and some performance measures of system are provided. Then the results are used to consider the performance of an ATM. Then to observe and optimize the performace of the ATM, we illustrate the effects of changing parameters on system performance measures. At last, we simulate the system by using the R application. Then we compare its results with expected results.
, , ,
Volume 22, Issue 1 (12-2017)
Abstract
Latent class analysis (LCA) is a method of evaluating non sampling errors, especially measurement error in categorical data. Biemer (2011) introduced four latent class modeling approaches: probability model parameterization, log linear model, modified path model, and graphical model using path diagrams. These models are interchangeable. Latent class probability models express likelihood of cross-classification tables in term of conditional and marginal probabilities for each cell. In this approach model parameters are estimated using EM algorithm. To test latent class model chi-square statistic is used as a measure of goodness-of-fit. In this paper we use LCA and data from a small-scale survey to estimate misclassification error (as a measurement error) of students who had at least a failing grade as well as misclassification error of students with average grades below 14.
Seyedeh Mona Ehsani Jokandan, Behrouz Fathi Vajargah,
Volume 24, Issue 2 (3-2020)
Abstract
In this paper, the difference between classical regression and fuzzy regression is discussed. In fuzzy regression, nonphase and fuzzy data can be used for modeling. While in classical regression only non-fuzzy data is used.
The purpose of the study is to investigate the possibility of regression method, least squares regression based on regression and linear least squares linear regression method based on fuzzy weight calculation for non-fuzzy input and fuzzy output using symmetric triangular fuzzy numbers. Further reliability, confidence intervals and fitness fit criterion is presented for choosing the optimal model.
Finally, by providing examples of the behavior of the proposed methods, the optimality of the regression hybrid model is shown by the least linear fuzzy squares.
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.
Ms. Zahra Jafarian Moorakani, Dr. Heydar Ali Mardani-Fard,
Volume 27, Issue 1 (3-2023)
Abstract
The ordinary linear regression model is $Y=Xbeta+varepsilon$ and the estimation of parameter $beta$ is: $hatbeta=(X'X)^{-1}X'Y$. However, when using this estimator in a practical way, certain problems may arise such as variable selection, collinearity, high dimensionality, dimension reduction, and measurement error, which makes it difficult to use the above estimator. In most of these cases, the main problem is the singularity of the matrix $X'X$. Many solutions have been proposed to solve them. In this article, while reviewing these problems, a set of common solutions as well as some special and advanced methods (which are less favored by someone, but still have the potential to solve these problems intelligently) to solve them.
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