Weighted Weibull Distribution Research Articles

Statistical image watermark decoder by modeling local RDWT difference domain singular values with bivariate weighted Weibull distribution

Statistical image watermark decoder by modeling local RDWT difference domain singular values with bivariate weighted Weibull distribution

A New Version of Weighted Weibull Distribution: Modelling to COVID‐19 Data

In this study, we will look at a new flexible model known as the new double‐weighted Weibull distribution. The new Weibull double‐weighted distribution model is highly versatile because numerous submodels are included. The proposed model is very flexible because its density function has many shapes; it can be right skewness, decreasing, and unimodal. Also, the hazard rate function can be increasing, decreasing, up‐side‐down, and J‐shaped. Diverse features of the novel are computed. These qualities include moments, incomplete moments, and Lorenz and Bonferroni curves and quantiles, as well as entropy and order statistics. The maximum likelihood approach is used to estimate the model's parameters. In order to evaluate the accuracy and performance of maximum likelihood estimators, simulation data are presented. The utility and adaptability of the proposed model are demonstrated by utilizing three significant datasets: daily fatalities confirmed cases of COVID‐19 in Egypt and Georgia and relief times of twenty patients using an analgesic.

Zero Truncated Negative Binomial Weighted Weibull Distribution and Its Application

Zero Truncated Negative Binomial Weighted Weibull Distribution and Its Application

Moments of Dual Generalized Order Statistics from Topp Leone Weighted Weibull Distribution and Characterization

Moments of Dual Generalized Order Statistics from Topp Leone Weighted Weibull Distribution and Characterization

A NEW CLASS OF TRANSMUTED MODIFIED WEIGHTED WEIBULL DISTRIBUTION AND ITS PROPERTIES

An additional parameter was added to Modified Weighted Weibull distribution with method of quadratic rank transmutation which led to a newly developed distribution called Transmuted Modified Weighted Weibull distribution. Two distributions that emanated from the new distribution are Transmuted Modified Rayleigh and Transmuted Modified Exponential distributions. Some properties of the distribution that were obtained include; the survival rate, hazard rate, reverse hazard rate function; and moment generating function, mean and variance. Also, parameters of the model were estimated using maximum likelihood estimation method. The model was applied to a life time data set of total milk production of the first birth of 107 cows which showed a better performance compared to some existing known distributions.

A New Generalized Weighted Weibull Distribution

In this article, we present a new generalization of weighted Weibull distribution using Topp Leone family of distributions. We have studied some statistical properties of the proposed distribution including quantile function, moment generating function, probability generating function, raw moments, incomplete moments, probability, weighted moments, Rayeni and q th entropy. The have obtained numerical values of the various measures to see the eect of model parameters. Distribution of of order statistics for the proposed model has also been obtained. The estimation of the model parameters has been done by using maximum likelihood method. The eectiveness of proposed model is analyzed by means of a real data sets. Finally, some concluding remarks are given.

Some properties of size - biased weighted Weibull distribution

Some properties of size - biased weighted Weibull distribution

Weighted Weibull distribution: Bivariate and multivariate cases

Gupta and Kundu (Statistics 43 (2009) 621–643) introduced a new class of weighted exponential distribution and established its several properties. The probability density function of the proposed weighted exponential distribution is unimodal and it has an increasing hazard function. Following the same line Shahbaz, Shahbaz and Butt (Pak. J. Stat. Oper. Res. VI (2010) 53–59) introduced weighted Weibull distribution, and we derive several new properties of this weighted Weibull distribution. The main aim of this paper is to introduce bivariate and multivariate distributions with weighted Weibull marginals and establish their several properties. It is shown that the hazard function of the weighted Weibull distribution can have increasing, decreasing and inverted bathtub shapes. The proposed multivariate model has been obtained as a hidden truncation model similarly as the univariate weighted Weibull model. It is observed that to compute the maximum likelihood estimators of the unknown parameters for the proposed $p$-variate distribution, one needs to solve $(p+2)$ non-linear equations. We propose to use the EM algorithm to compute the maximum likelihood estimators of the unknown parameters. We obtain the observed Fisher information matrix, which can be used for constructing asymptotic confidence intervals. One data analysis has been performed for illustrative purposes, and it is observed that the proposed EM algorithm is very easy to implement, and the performance is quite satisfactory.

Some properties of volume-biased weighted Weibull distribution

Some properties of volume-biased weighted Weibull distribution

A new generalized weighted Weibull distribution with decreasing, increasing, upside-down bathtub, N-shape and M-shape hazard rate

ABSTRACTRecently, Domma et al. [An extension of Azzalinis method, J. Comput. Appl. Math. 278 (2015), pp. 37–47] proposed an extension of Azzalini's method. This method can attract readers due to its flexibility and ease of applicability. Most of the weighted Weibull models that have been introduced are with monotonic hazard rate function. This fact limits their applicability. So, our aim is to build a new weighted Weibull distribution with monotonic and non-monotonic hazard rate function. A new weighted Weibull distribution, so-called generalized weighted Weibull (GWW) distribution, is introduced by a method exposed in Domma et al. [13]. GWW distribution possesses decreasing, increasing, upside-down bathtub, N-shape and M-shape hazard rate. Also, it is very easy to derive statistical properties of the GWW distribution. Finally, we consider application of the GWW model on a real data set, providing simulation study too.

On the Estimation of Reliability of Weighted Weibull Distribution: A Comparative Study

The paper gives a description of estimation for the reliability function of weighted Weibull distribution. The maximum likelihood estimators for the unknown parameters are obtained. Nonparametric methods such as empirical method, kernel density estimator and a modified shrinkage estimator are provided. The Markov chain Monte Carlo method is used to compute the Bayes estimators assuming gamma and Jeffrey priors. The performance of the maximum likelihood, nonparametric methods and Bayesian estimators is assessed through a real data set.

Some Statistical Properties of Exponentiated Weighted Weibull Distribution

Some Statistical Properties of Exponentiated Weighted Weibull Distribution

Weighted Weibull Distribution: Properties and Estimation

We take a closer look at the weighted Weibull distribution. First, we study the structural properties of the probability density function, hazard rate, and mean residual lifetime functions of this distribution. We put forward the estimation for the parameters of the weighted Weibull distribution via maximum likelihood estimation technique. We also obtain expected Fisher’s information matrix, as well as discuss the existence and uniqueness of the maximum likelihood estimates. With regard to Bayesian inference of the unknown parameters, we are using importance sampling technique to calculate Bayes estimates and the corresponding highest posterior density intervals. We perform a data analysis for illustrative purposes.

English

We upgrade on the method used on weighted Weibull model proposed by Azzalini (1985) using the logit of Beta function by Jones (2004) to have Lehmann Type II weighted Weibull model with a view to obtaining a distribution that is more better than both weighted Weibull and Weibull distribution in terms of estimate of their characteristics and their parameter. The weighted Weibull distribution is proposed by slightly modifying the method of Azzalini (1985) on weighted distribution with additional shape  and scale  parameters. Some basic properties of the newly proposed distribution including moments and moment generating function, survival rate function, hazard rate function, asymptotic behaviours, and the estimation of parameters have been studied. Key words: Asymptotic, hazard rate, Lehmann Type II, moments, weighted-Weibull.

The Beta-Weighted Weibull Distribution: Some Properties and Application to Bladder Cancer Data

Combination of weighted weibull distribution proposed by Ramadan and beta distribution gives a better distribution (beta-weighted weibull distribution) than each of them individually in terms of the estimate of their characteristics in their parameters. The combination was done by using the logic of beta function by Jones. We investigated in the new proposed beta-weighted weibull distribution some basic properties including moments, moment generating functions, survival rate function, and hazard rate function, skewness, and kurtosis, coefficient of variation, asymptotic behaviors and estimation of parameters. The proposed model is much more flexibility and has better representation of data than weighted weibull distribution. A real data set is used to illustrate the importance the potentiality of the new model.

A Class of Weighted Weibull Distribution

The weighted Weibull model is proposed following the method of Azzalini (1985). Basic properties of the distribution; including moments, generating function, hazard rate function and estimation of parameters; have been studied.

Queuing Models for Dimensioning Interactive and Streaming Services in High-Speed Downlink Packet Access Networks

We consider modeling the statistical behavior of interactive and streaming traffics in high-speed downlink packet access (HSDPA) networks. Two important applications in these traffic categories are web-browsing (interactive service) and video streaming (streaming service). Web-browsing is characterized by its important sensitivity to delay. Video streaming on the other hand is less sensitive to delay, however, due to its large frame sizes, video traffic is more affected by the packet loss resulting from a limited buffer size at the base station. Taking these characteristics into account, we consider modeling the queuing delay probability density function (PDF) of the Web-browsing traffic, and modeling the queuing buffer size distribution of video streaming traffic. Specifically, we show that the queuing delay of the Web-browsing traffic follows an exponential distribution and that the queuing buffer size of video streaming traffic follows a weighted Weibull distribution. Model fitting based on simulated data is used to provide simple mathematical formulations for the different parameters that characterize the PDFs under consideration. The provided equations could be used, directly, in HSDPA network dimensioning and, as a reference, to satisfy a certain quality of service (QoS).