parameter estimation

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.

In this paper, features and applications of a four-parameter distribution called "Exponentiated Log-Logistic Weibull" distribution are presented, which has many applications in meteorology, life time issues, agricultural product price modeling, and reliability. This distribution has an increasing, decreasing, u-shaped and inverted u-shaped hazard rate function. In this article, at first the statistical and mathematical characteristics of this distribution are presented, including moments, maximum likelihood estimation of parameters, deviation from the mean and median, Bonferroni and Lorenz curve. Finally its applications are presented to show the flexibility of the distribution. The data fitting results also confirm the suitability of this model for the considered real data set.

In this paper a new four-parameter lifetime distribution named “the exponentiated Lomax – Rayleigh (E-LR) distribution” has been suggested that it has an increasing hazard rate for modeling lifetime data. The Lomax distribution has applications in economics, actuarial modelling, reliability modeling, lifetime and queuing problems and biological sciences. In this paper Firstly, the mathematical and statistical characteristics of the proposed distribution are presented, then the applications of the new distribution are studied using the real data set. Its first moment about origin and moments about mean have been obtained and expressions for skewness, kurtosis has been given. Various mathematical and statistical properties of the proposed distribution have been discussed. Estimation of its parameter has been discussed using the method of maximum likelihood. In the end, two applications of the new distribution have been discussed with two real lifetime data sets. The results also confirmed the suitability of the presented models for real data collection.

One of the income distributions is the generalized Beta distribution. In this paper the family of generalized Beta distribution is introduced and it was investigated in terms of maximum entropy. Also, using MATLAB the maximum entropy estimator of the parameters of this family was found, considering variety of conditions.

In this paper, a skew normal state space model of RC electrical circuit is presented by considering the stochastic differential equation of the this circuit as the dynamic model with colored and white noise and considering a skew normal distribution instead of normal as the measurement noise distribution. Optimal filtering technique via sequential Monte Carlo perspective is developed for tracking the charge as the hidden state of this model. Furthermore, it is assumed that this model contains unknown parameters (resistance, capacitor, mean, variance and shape parameter of the skew normal as the measurement noise distribution). Bayesian framework is applied for estimation of both the hidden charge and the unknown parameters using particle marginal Metropolis-Hastings scheme. It is shown that the coverage percentage of skew normal is more than the one of normal as the measurement noise. Some simulation studies are carried out to demonstrate the efficiency of the proposed approaches.