scholarly journals On Wrapping of Quasi Lindley Distribution

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 930
Author(s):  
Ahmad M. H. Al-khazaleh ◽  
Shawkat Alkhazaleh
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Cumulative Distribution ◽  
Estimation Of Parameters ◽  
Circular Distribution ◽  
Lindley Distribution ◽  
Trigonometric Moments ◽  
Maximum Likelihood Estimation Method

In this paper, as an extension of Wrapping Lindley Distribution (WLD), we suggest a new circular distribution called the Wrapping Quasi Lindley Distribution (WQLD). We obtain the probability density function and derive the formula of a cumulative distribution function, characteristic function, trigonometric moments, and some related parameters for this WQLD. The maximum likelihood estimation method is used for the estimation of parameters.

scholarly journals The Maxwell – Exponential Distribution: Theory and Application to Lifetime Data

2021 ◽  
Vol 3 (2) ◽  
pp. 65-80
Author(s):  
Usman Aliyu Abdullahi ◽  
Ahmad Abubakar Suleiman ◽  
Aliyu Ismail Ishaq ◽  
Abubakar Usman ◽  
Aminu Suleiman
Keyword(s):  
Exponential Distribution ◽  
Survival Function ◽  
Real Life ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Quantile Function ◽  
Information Criteria ◽  
Cumulative Distribution ◽  
Two Parameters ◽  
Maximum Likelihood Estimation Method

Two parameters Maxwell – Exponential distribution was proposed using the Maxwell generalized family of distribution. The probability density function, cumulative distribution function, survival function, hazard function, quantile function, and statistical properties of the proposed distribution are discussed. The parameters of the proposed distribution have been estimated using the maximum likelihood estimation method. The potentiality of the estimators was shown using a simulation study. The overall assessment of the performance of Maxwell - Exponential distribution was determined by using two real-life datasets. Our findings reveal that the Maxwell – Exponential distribution is more flexible compared to other competing distributions as it has the least value of information criteria.

scholarly journals On the Properties and Applications of Transmuted Odd Generalized Exponential-Exponential Distribution

2018 ◽  
pp. 1-14
Author(s):  
Jamila Abdullahi ◽  
Umar Kabir Abdullahi ◽  
Terna Godfrey Ieren ◽  
David Adugh Kuhe ◽  
Adamu Abubakar Umar
Keyword(s):  
Distribution Function ◽  
Probability Density Function ◽  
Exponential Distribution ◽  
Cumulative Distribution Function ◽  
Real Life ◽  
Likelihood Estimation ◽  
Cumulative Distribution ◽  
Method Of Maximum Likelihood ◽  
Probability Density Function Pdf ◽  
New Distribution

This article proposed a new distribution referred to as the transmuted odd generalized exponential-exponential distribution (TOGEED) as an extension of the popular odd generalized exponential- exponential distribution by using the Quadratic rank transmutation map (QRTM) proposed and studied by [1]. Using the transmutation map, we defined the probability density function (pdf) and cumulative distribution function (cdf) of the transmuted odd generalized Exponential- Exponential distribution. Some properties of the new distribution were extensively studied after derivation. The estimation of the distribution’s parameters was also done using the method of maximum likelihood estimation. The performance of the proposed probability distribution was checked in comparison with some other generalizations of Exponential distribution using a real life dataset.  

scholarly journals A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 835 ◽  
Author(s):  
Lorentz Jäntschi
Keyword(s):  
Continuous Distribution ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Sampling Distribution ◽  
Cumulative Distribution ◽  
Experimental Science ◽  
Continuous Distributions ◽  
Statistical Parameters ◽  
Rich Information ◽  
Maximum Likelihood Estimation Method

One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from which it was drawn. To answer this question, a series of statistics have been developed to measure the agreement between the theoretical (the population) and observed (the sample) distributions. Another challenge, connected to this, is the presence of outliers - regarded here as observations wrongly collected, that is, not belonging to the population subjected to study. To detect outliers, a series of tests have been proposed, but mainly for normal (Gauss) distributions—the most frequently encountered distribution. The present study proposes a statistic (and a test) intended to be used for any continuous distribution to detect outliers by constructing the confidence interval for the extreme value in the sample, at a certain (preselected) risk of being in error, and depending on the sample size. The proposed statistic is operational for known distributions (with a known probability density function) and is also dependent on the statistical parameters of the population—here it is discussed in connection with estimating those parameters by the maximum likelihood estimation method operating on a uniform U(0,1) continuous symmetrical distribution.

scholarly journals Inverse Analogue of Ailamujia Distribution with Statistical Properties and Applications

2020 ◽  
pp. 36-46
Author(s):  
Ahmad Aijaz ◽  
Afaq Ahmad ◽  
Rajnee Tripathi
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Real Life ◽  
Likelihood Estimation ◽  
Moment Generating Function ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Life Data ◽  
Real Life Data ◽  
Method Of Maximum Likelihood

The present paper deals with the inverse analogue of Ailamujia distribution (IAD). Several statistical properties of the newly developed distribution has been discussed such as moments, moment generating function, survival measures, order statistics, shanon entropy, mode and median .The behavior of probability density function (p.d.f) and cumulative distribution function (c.d.f) are illustrated through graphs. The parameter of the newly developed distribution has been estimated by the well known method of maximum likelihood estimation. The importance of the established distribution has been shown through two real life data.

scholarly journals The Beta Type I Generalized Half Logistic Distribution: Properties and Application

2020 ◽  
pp. 27-41
Author(s):  
P. O. Awodutire ◽  
E. C. Nduka ◽  
M. A. Ijomah
Keyword(s):  
Survival Function ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Logistic Distribution ◽  
Bladder Cancer Patient ◽  
Type I ◽  
Estimation Of Parameters ◽  
Survival Times ◽  
Maximum Likelihood Estimation Method ◽  
New Distribution

In a view to obtain a new distribution that is more exible than the type I generalized half logistic distribution, we used the beta-G generator and the type I generalized half logistic distribution. Some properties of the new distribution including the cummulative distribution function,survival function, hazard function were studied. Estimation of parameters were done using the maximum likelihood estimation method. Application of the derived distribution to lifetime data was illustrated by applying to remission times of bladder cancer patient data and survival times of guinea pigs.

On Log-q-Gaussian Distribution

2018 ◽  
Vol 70 (2) ◽  
pp. 105-121
Author(s):  
Ken-ichi Koike ◽  
Yuya Shimegi
Keyword(s):  
Distribution Function ◽  
Gaussian Distribution ◽  
Cumulative Distribution Function ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Random Variable ◽  
Cumulative Distribution ◽  
Ams Classification ◽  
New Distribution

We propose the log- q-Gaussian distribution which is obtained as the distribution of a random variable whose logarithm is q-Gaussian. Various types of properties of the new distribution are given such as the moments, the cumulative distribution function, the extreme value distribution, the likelihood estimation, and so on. Some examples for real data are also given. AMS Classification: 60Exx 62Exx.

A Method to Quantify Risks of Financial Assets: An Empirical Analysis of Japanese Security Prices

2012 ◽  
Vol 452-453 ◽  
pp. 469-473 ◽  
Author(s):  
Aki Hiro Sato
Keyword(s):  
Distribution Function ◽  
Value At Risk ◽  
Cumulative Distribution Function ◽  
Stock Exchange ◽  
Characteristic Exponent ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Analytical Form ◽  
Cumulative Distribution ◽  
Security Prices

This study investigates unconditional distributions of daily log-returns of Japanese security prices from a comprehensive point of view. The purpose of this article is to estimate a risk distribution of stocks in terms of Value-at-Risk (VaR) in order to select low risk securities from many securities. Daily log-return time series of 1,340 Japanese companies listed on the first section of Tokyo Stock Exchange are examined during the last one decade. I develop a method to estimate VaR by both the maximum likelihood estimation procedure under a q-Gaussian assumption and analytical form of its cumulative distribution function. It is confirmed that they are fitted to q-Gaussian distributions (Student t-distributions) with Kolmogorov-Smirnov test. It is found that the complementary cumulative distribution function of VaR has a power-law tail with its characteristic exponent depending on values of the VaR percentile.

scholarly journals A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

METRON ◽  
2021 ◽  
Author(s):  
Carlo Cavicchia ◽  
Pasquale Sarnacchiaro
Keyword(s):  
Job Satisfaction ◽  
Factor Analysis ◽  
Secondary School Teachers ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Higher Order ◽  
Teacher Job Satisfaction ◽  
Public Secondary School ◽  
Order Factor ◽  
Maximum Likelihood Estimation Method

AbstractTeachers’ performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers’ accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Common Assessment Framework and Education questionnaire was administered to 163 Italian public secondary school teachers to collect data, and a second-order factor analysis was used to detect which factors impact on Teacher Job Satisfaction, and to what extent. This model-based approach guarantees to detect factors which respect important properties: unidimensionality and reliability. All the coefficients are estimated according to the maximum likelihood estimation method in order to make inference on the parameters and on the validity of the model. Moreover, a new multi-group test for higher-order factor analysis was proposed and implemented. Finally, we analyzed in detail whether the factors impacting Teacher Job Satisfaction are characterized by gender.

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