Ateya and Madhagi (2011) introduced a multivariate form of truncated generalized Cauchy distribution (TGCD), which introduced by Ateya and Al-Hussaini (2007). The multivariate version of (TGCD) is denoted by (MVTGCD). Among the features of this form are that subvectors and conditional subvectors of random vectors, distributed according to this distribution, have the same form of distribution (MVTGCD). They also introduced the joint density function, conditional density function, moment generating function and mixed moments. Also, they estimated all parameters of the distribution using the maximum likelihood and Bayes methods. In this paper, we used the point of view, introduced by Al-Hussaini and Ateya (2010), to obtain the Highest Posterior Density (HPD) prediction intervals of future observations from bivariate truncated generalized Cauchy distribution (BVTGCD).

Let X1,X2, ...,Xr be the first r order statistics from a sample of size n from the generalized exponential distribution with shape parameter q. In this paper, we consider a Bayesian approach to predicting future order statistics based on the observed ordered data. The predictive densities are obtained and used to determine prediction intervals for unobserved order statistics for one-sample and two-sample prediction plans. A numerical study is conducted to illustrate the prediction procedures.

This paper is devoted to compute the sample size for estimation of Normal distribution mean with Bayesian approach. The Quadratic loss function is considered and three criterions are applied to obtain p- tolerance regions with the lowest posterior loss. These criterions are: average length, average coverage and worst outcome. The proposed methodology is examined, and its effectiveness is shown.

Background: Breast cancer is the second leading cause of death from cancer among women, and many factors are involved in its creation. The purpose of this study was to evaluate the effect of tumor markers on the survival of women with this cancer using Bayesian cure analysis. Methods: This was a population-based cohort study on 500 women with breast cancer registered in Shahid Ramazanzadeh hospital, Yazd City, Iran, from the April 2010 until March 2015, using Kaplan-Meier method and Bayesian cure model. The data were analyzed using R software. P < 0. 050 was considered as the significance level. Findings: Based on Kaplan-Meier method, the 6-year cumulative survival for patients with breast cancer was 0. 737. The mean age of breast cancer diagnosis was 48. 03 ± 11. 16 years, and the mean survival period was 97. 64 ± 4. 23 months. Bayesian cure model showed that Ki67 [hazard ratio (HR) = 1. 34, 95% prediction interval (PI): 1. 01-2. 28] and ER (HR = 2. 11, PI 95%: 1. 99-2. 36) were significantly related to hazard, and ER was significantly related to cure (OR = 0. 38, PI 95%: 0. 26-0. 57). Conclusion: According to Bayesian cure analysis in this study, ER variable is also effective on short-term survival and long-term survival of patients. Cure models have the ability to analyze patients’ survival data, and can differentiate long-term survival from short-term survival. The interpretation of survival data with these statistical models could be more accurate.

Purpose: Inflation has been a persistent and critical challenge in Iran's economy, necessitating Inflation has been a persistent and critical challenge in Iran's economy, necessitating a comprehensive understanding of its drivers and mechanisms for effective policy formulation. This study focuses on the interconnected roles of the key monetary policy tools, including liquidity, exchange rates, interest rates and base money, in inflation dynamics. Drawing upon the global perspective of inflation control, which includes exchange rate targeting, monetary aggregates and inflation targeting, the research adapts these insights to Iran's unique economic context.Using a Bayesian TVC-VAR model, the study captures the structural shifts and the evolving relationships among the corresponding variables from May 2014 to August 2024. The model’s strength lies in addressing the complex and interdependent nature of Iran’s macroeconomic variables, where changes in one variable can significantly impact others.The research highlights two pivotal periods: May 2018, marked by the U.S. withdrawal from the JCPOA, and August 2020, shaped by intensified sanctions and the COVID-19 pandemic. These periods underscore the importance of adaptive and coordinated monetary and exchange rate policies. The findings provide policymakers with workable insights to mitigate inflationary pressures and foster economic stability in an unpredictable environment.Methodology: This study employs the Bayesian TVC-VAR model, which builds on the traditional VAR framework by incorporating time-varying coefficients, enabling it to capture the evolving nature of economic relationships. The model uses monthly data spanning May 2014 to August 2024, sourced from the Central Bank of Iran. The key variables include the following:Inflation (CPI): A proxy for the general price level in the economyLiquidity: Representing the total money supply, influenced by the base money multiplier effectBase Money: High-powered money, including currency in circulation and bank reservesExchange Rate: The value of the Iranian Rial against foreign currenciesInterest Rate: Represented by the interbank rate, a proxy for general borrowing costsThe data were log-transformed and seasonally adjusted to ensure consistency. Unit root tests confirmed stationarity for all the variables except the interest rate, which required adjustments for structural breaks. To identify structural shocks, Cholesky decomposition was done, with the variables ordered as base money, exchange rate, liquidity, interest rate, and inflation. This hierarchy reflects the foundational role of base money in driving monetary aggregates and the lagged responses of inflation.The study focuses on two critical periods as follows:May 2018: The U.S. withdrawal from the JCPOA, which triggered significant economic and political uncertainty, leading to heightened exchange rate volatility and inflationary pressuresAugust 2020: A period of compounded economic crises due to the COVID-19 pandemic and intensified sanctions, which further disrupted economic stabilityFindings and discussion: The analysis reveals significant insights into the relationships between inflation and the key monetary policy variables. Shocks to liquidity had a moderate and short-lived impact on inflation. Following an increase in liquidity, inflation rose moderately, peaking after a few periods before stabilizing.The influence of liquidity was less pronounced than that of base money or exchange rate shocks, suggesting that the impact of liquidity on inflation is mediated through other variables.Exchange rate shocks exhibited the most immediate and significant impact on inflation. Depreciation of the Rial directly increased import prices, leading to a sharp and immediate rise in inflation.The magnitude of the impact was particularly pronounced in August 2020 due to combined economic crises. Inflation surged following an exchange rate shock and stabilized after approximately five periods.Interest rate shocks negatively affected inflation, demonstrating their effectiveness as a tool for controlling price levels. An increase in the interbank interest rate reduced inflation by curbing borrowing and aggregate demand.The effects of interest rate shocks were temporary, lasting for about 10 periods in May 2018 and 5 periods in August 2020 before inflation returned to its baseline.Shocks to base money had a pronounced and prolonged impact on inflation. Increases in base money, often used to finance government deficits, led to rapid inflationary pressures that persisted longer than those caused by liquidity shocks.The findings emphasize the critical role of base money as a driver of inflation, both directly and through its effects on liquidity and aggregate demand.Comparing the two critical periods, the study found consistent qualitative responses across the variables but differing magnitudes. The responses were more intense in August 2020, reflecting the heightened economic instability due to the combined effects of the COVID-19 pandemic and the external sanctions. The exchange rate shocks consistently had the most substantial impact on inflation, highlighting the critical importance of exchange rate stability in managing price levels.Conclusions and Policy Implications: This study underscores the significant role of monetary policy tools in shaping inflation dynamics in Iran. The Bayesian TVC-VAR model provided a nuanced understanding of the evolving relationships between inflation, exchange rates, interest rates, liquidity, and base money, offering valuable insights for policymakers:Exchange rate shocks have significant effects on inflation, making effective exchange rate management a policy priority. Implementing mechanisms to mitigate sharp currency fluctuations can play a crucial role in stabilizing prices and preventing sudden inflationary surges. This effort requires precise coordination between monetary and exchange rate policies.Given the positive impact of the interbank interest rate on controlling inflation, policymakers are advised to use this tool strategically while considering market conditions. Smart use of interest rates can help curb inflation by managing liquidity and channeling financial resources into productive economic sectors.The findings indicate that base money and liquidity shocks have substantial effects on inflation. Therefore, policymakers must closely monitor the growth of these two variables and prevent their excessive expansion. This requires implementing appropriate monetary policies and enhancing financial transparency to strengthen public trust and optimize policy effectiveness.The rapid response of the variables to exchange rate shocks underscores the need for policymakers to establish mechanisms and structures to quickly identify these shocks and adopt effective, timely responses. Building such frameworks, especially during crises, can prevent severe economic fluctuations and minimize their negative impacts.

History and Objectives: Due to importance of preterm labor problems and its complications and regarding the controversies on the relationship of interpregnancy intervals of less than 15 months and preterm labor and in order to define effect of interpregnancy interval of less than 15 months on preterm labor, the present study was performed at Shabikhani Maternity Hospital during 1997-1998.Materials and Methods: In a case study, control group (N=100) consisted labors after 37 weeks of pregnancies and a case group (N=70) of labors before 37 weeks of pregnacy. Interpregnancy interval of less than 15 months in two groups after termination of preceding pregnancy and start of next pregnancy was evaluated. Case and control groups were matched for influencing factors on preterm labor. Chi-square statistical analysis was used to test the results and Odd's ratio were determined.Results: No significant differences were observed among case and control groups (N =100). Short interpregnancy interval was 9% and 21.4% among case and control group respectively (p < 0.02). Preterm labor was 2.7 time higher among women with interpregnancy interval of less than 15 month. Conclusions: Interpregnacy interval of less than 15 months has an effect on incidence of preterm labor. Further research in required to pinpoint the factors involved with the short interpregnancy interval.

A new continuous distribution called Lindley-Lindley distribution is defined and studied. Relevant mathematical properties are derived. We present three characterizations of the new distribution based on the truncated moments of certain functions of the random variable,the hazard function and in terms of the conditional expectation of a function of the random variable. Some new bivariate type distributions using Farlie Gumbel Morgenstern copula, modified Farlie Gumbel Morgenstern copula and Clayton copula are introduced. The main justification of this paper is to show how different frequentist estimators of the new model perform for different sample sizes and different parameter values and to provide a guideline for choosing the best estimation method for the parameters of the proposed model. The unknown parameters of the new distribution are estimated using the maximum likelihood, ordinary least squares, Cramer-Von-Mises, weighted least squares and Bayesian methods. The obtained estimators are compared using Markov Chain Monte Carlo simulations and observed that Bayesian estimators are generally more efficient than the other estimators.