جستجوی مقالات مرتبط با کلیدواژه « moments » در نشریات گروه « آمار »

In this paper, the concept of unit generalized Gompertz (UGG) distribution will be introduced as a new transformed model of the unit Gompertz distribution, which contains the unit Gompertz distribution as a special case. We calculate explicit expressions for the moments, moment generating, quantile, and hazard functions, and Tsallis and R'{e}nyi entropy. Some different methods for estimation and inference about model parameters are presented too. To estimate the unknown parameters of the model, the maximum likelihood, maximum product spacings, and bootstrap sampling have been discussed, and also approximate confidence interval is presented. Finally, a simulation study and an application to a real data set are given.

 In this paper a new three-parameter lifetime distribution named “the Exponentialed Lindley-Poisson (E-LP) distribution” has been suggested that it has an  increasing, decreasing and invers bathtube hazard rate depending on the parameter values. The (E-LP) distribution has applications in economics, actuarial modeling, reliability modeling, lifetime and queuing problems and biological sciences. 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. expressions for moments, skewness and kurtosis have 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. A simulation study is given. 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.

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.

‎In this paper‎, ‎a new distribution is introduced‎, ‎which is a generalization of a well-known distribution‎. ‎This distribution is flexible and applies to income data modeling‎. ‎We first provide some of the mathematical and distributional properties of this new model and then‎, ‎to demonstrate the flexibility the new distribution‎, ‎we will present the applications of this distribution with real data‎. ‎Data fitting results confirm the appropriateness of this new model for the real data set‎.

We propose a new class of continuous models called the Weibull Generalized G family with two extra positive shape parameters, which extends several well-known models. We obtain some of its mathematical properties including ordinary and incomplete moments, generating function, order statistics, probability weighted moments, entropies, residual, and reversed residual life functions. Characterizations based on a ratio of two truncated moments, in terms of hazard function and based on certain functions of the random variable are presented. We estimate the model parameters by the maximum likelihood method. We assess the performance of the maximum likelihood estimators in terms of biases and mean squared errors by means of two simulation studies. The usefulness of the proposed models is illustrated via three real data sets.

In this paper, first, we investigate probability density function and the failure rate function of some families of exponential distributions. Then we present their features such as expectation, variance, moments and maximum likelihood estimation and we identify the most flexible distributions according to the figure of probability density function and the failure rate function and finally we offer practical examples of them.

In this paper we propose a new distribution based on Weibull distribution. This distribution has three parameters which displays increasing, decreasing, bathtub shaped, unimodal and increasing-decreasing-increasing failure rates. Then consider characteristics of this distribution and a real data set is used to compared proposed distribution whit some of the generalized Weibull distribution.

This article first introduces the sterling polynomials and expresses the calculation methods in software R and their application in resolving a class of integrals which play an important role in the calculation of torques, torques of order statistics, Renyi and Shannon entropies in statistical distributions based on this type of integrals. Then, we obtain the torques of the two statistical distributions related to these integrals.

In this paper a new distribution function based on Weibull distribution is introduced. Then the characteristics of this new distribution are considered and a real data set is used to compare this distribution with some of the generalized Weibull distributions.

Mathematical methods and statistical distributions present exact results in the climate calculations and hydrological processes. Awareness of the rainfall probability distribution provides the appropriate conditions for water resource planning. Many studies have been done to estimate probability of rainfall by various methods due to the importance of rainfall distribution in the economic, social and particularly agriculture studies. In these studies, the various probabilistic models have been used and the results of the most investigations show that the bivariate gamma distribution branches of gamma model are compatible for rainfall data. The bivariate gamma distribution is used in the hydrological processes modeling. In the present paper, supposing that the X and Y follow the crovelli’s bivariate gamma model, at first a brief description was given in the case of the exact distributions of the functions U=X+Y, P=XY and Q=X⁄((X+Y)) as well as their respective moments, then the validity of this model was evaluated for Rasht airport weather station data. The results showed that rainfall data of this region also confirms The suitability of the crovelli’s bivariate gamma model.