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Showing 42 results for Subject:
Arezu Rahmanpour, Yadollah Waghei, Gholam Reza Mohtashami Borzadaran,
Volume 19, Issue 1 (9-2025)
Abstract
Change point detection is one of the most challenging statistical problems because the number and position of these points are unknown. In this article, we will first introduce the concept of change point and then obtain the parameter estimation of the first-order autoregressive model AR(1); in order to investigate the precision of estimated parameters, we have done a simulation study. The precision and consistency of parameters were evaluated using MSE. The simulation study shows that parameter estimation is consistent. In the sense that as the sample size increases, the MSE of different parameters converges to zero. Next, the AR(1) model with the change point was fitted to Iran's annual inflation rate data (from 1944 to 2022), and the inflation rate in 2023 and 2024 was predicted using it.
Tara Mohammadi, Hadi Jabbari, Sohrab Effati,
Volume 19, Issue 1 (9-2025)
Abstract
Support vector machine (SVM) as a supervised algorithm was initially invented for the binary case, then due to its applications, multi-class algorithms were also designed and are still being studied as research. Recently, models have been presented to improve multi-class methods. Most of them examine the cases in which the inputs are non-random, while in the real world, we are faced with uncertain and imprecise data. Therefore, this paper examines a model in which the inputs are uncertain and the problem's constraints are also probabilistic. Using statistical theorems and mathematical expectations, the problem's constraints have been removed from the random state. Then, the moment estimation method has been used to estimate the mathematical expectation. Using Monte Carlo simulation, synthetic data has been generated and the bootstrap resampling method has been used to provide samples as input to the model and the accuracy of the model has been examined. Finally, the proposed model was trained with real data and its accuracy was evaluated with statistical indicators. The results from simulation and real examples show the superiority of the proposed model over the model based on deterministic inputs.
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