آرش نادمی

Objective: The aim of this study was to compare the effectiveness of schema-focused couple therapy and emotion-focused couple therapy on marital offense forgiveness in couples with marital discrepancies in Ilam city. Method: This was a quasi-experimental study with a pre-test, post-test, and follow-up design with a control group. The study population comprised 105 couples who sought counseling in Ilam during the second half of the year 2021. From this population, 30 couples were selected using a convenience sampling method and randomly assigned to three groups: emotion-focused intervention (10 couples), schema-focused intervention (10 couples), and a control group (10 couples). The emotion-focused group received Johnson's training package (2012) over 9 sessions, and the schema-focused group received the Young, Klosko, & Weishaar package (2003) also over 9 sessions. The control group did not receive any intervention. The research instrument used was the Marital Offense Forgiveness Scale by Paleari, Regalia, and Fincham (2009). Data were analyzed using repeated measures analysis of variance. Findings: Schema-focused couple therapy had a significant effect on marital offense forgiveness (F = 5.85, p = .025). Emotion-focused couple therapy also had a significant effect on marital offense forgiveness (F = 5.81, p = .045). These effects were reported to be stable at the follow-up stage. Additionally, there was no significant difference in the effectiveness of schema-focused couple therapy versus emotion-focused couple therapy on marital offense forgiveness. Conclusion: Both schema-focused and emotion-focused couple therapies are effective methods for enhancing marital offense forgiveness in couples with marital discrepancies.

The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.

The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Markov Switching models for forecasting the time series observations based on stock prices. The semi-parametric estimators for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of estimation. The main part of these estimators is based on kernel functions. Despite the existence of many kernel functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel in these estimators. But there is a question if other types of kernel function can be used in these estimators. This paper tries to introduce the other kernel functions that can be a good replacement for this kernel function to increase the ability of Markov Switching models. We first test six popular kernel functions to find the best one based on simulation studies and then offer the new strategy of buying and selling stocks by the best kernel function selection on real data.