scholarly journals Normal-G Class of Probability Distributions: Properties and Applications

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1407 ◽  
Author(s):  
Fábio V. J. Silveira ◽  
Frank Gomes-Silva ◽  
Cícero C. R. Brito ◽  
Moacyr Cunha-Filho ◽  
Felipe R. S. Gusmão ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distributions ◽  
Moment Generating Function ◽  
Maximum Likelihood Estimates ◽  
Simulation Studies ◽  
Mathematical Properties ◽  
Parsimonious Models ◽  
The Moment ◽  
Probability Density Function Pdf

In this paper, we propose a novel class of probability distributions called Normal-G. It has the advantage of demanding no additional parameters besides those of the parent distribution, thereby providing parsimonious models. Furthermore, the class enjoys the property of identifiability whenever the baseline is identifiable. We present special Normal-G sub-models, which can fit asymmetrical data with either positive or negative skew. Other important mathematical properties are described, such as the series expansion of the probability density function (pdf), which is used to derive expressions for the moments and the moment generating function (mgf). We bring Monte Carlo simulation studies to investigate the behavior of the maximum likelihood estimates (MLEs) of two distributions generated by the class and we also present applications to real datasets to illustrate its usefulness.

scholarly journals A New Generalized Weibull- Odd Frѐchet Family of Distributions: Statistical Properties and Applications

2020 ◽  
pp. 25-43
Author(s):  
A. Usman ◽  
S. I. S. Doguwa ◽  
B. B. Alhaji ◽  
A. T. Imam
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Order Statistics ◽  
Mean Squared Error ◽  
Moment Generating Function ◽  
Data Sets ◽  
Mathematical Properties ◽  
Squared Error ◽  
New Distribution ◽  
Family Of Distributions

We introduced a new generalized Weibull- Odd Frѐchet family of distributions with three extra parameters and we derived some of its structural properties. We derived comprehensive mathematical properties which include moments, moment generating function, Entropies and Order Statistics. One family of this distribution called new generalized Weibull- Odd Frѐchet -Frѐchet distribution is used to fit two data sets using the MLE procedure. A Monte Carlo simulation is used to test the robustness of the parameters of this distribution, in terms of the bias and mean squared error. The results of fitting this new distribution to two different data sets suggest that the new distribution outperforms its competitors.

scholarly journals The Alpha-Beta Skew Generalized t Distribution: Properties and Applications

2019 ◽  
pp. 605-616
Author(s):  
Fazlollah Lak ◽  
Morad Alizadeh ◽  
Mohammad Esmaeil Dehghan Monfared ◽  
Hamid Esmaeili
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Asymptotic Properties ◽  
Likelihood Estimation ◽  
Moment Generating Function ◽  
Alpha Beta ◽  
The Moment ◽  
T Distribution ◽  
New Distribution

n this paper, we propose a new distribution, namely alpha-beta-skew generalized tdistribution. The proposed distribution is really flxible and includes as special models some important distributions like Normal, t-student, Cauchy and etc as its marginal component distributions. It features a probability density function with up to three modes. The moment generating function as well as the main moments are provided. Inference is based on a usual maximum-likelihood estimation approach and a small Monte Carlo simulation is conducted for studying the asymptotic properties of the maximum-likelihood estimate. The usefulness of the new model is illustrated in a real data

scholarly journals The normal-tangent-G class of probabilistic distributions: properties and real data modellin

2020 ◽  
pp. 827-838
Author(s):  
Fábio Silveira ◽  
Frank Gomes-Silva ◽  
Cícero Brito ◽  
Moacyr Cunha-Filho ◽  
Jader Jale ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Probability Density Function ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Series Representation ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Special Cases ◽  
Probability Density Function Pdf

This paper introduces a novel class of probability distributions called normal-tangent-G, whose submodels are parsi- monious and bring no additional parameters besides the baseline’s. We demonstrate that these submodels are iden- tifiable as long as the baseline is. We present some properties of the class, including the series representation of its probability density function (pdf) and two special cases. Monte Carlo simulations are carried out to study the behav- ior of the maximum likelihood estimates (MLEs) of the parameters for a particular submodel. We also perform an application of it to a real dataset to exemplify the modelling benefits of the class.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

Monte Carlo simulation studies of the catalytic combustion of methane

2006 ◽  
Vol 112 (1-2) ◽  
pp. 121-128 ◽  
Author(s):  
Joaquín Cortés ◽  
Eliana Valencia ◽  
Paulo Araya
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Catalytic Combustion ◽  
Simulation Studies ◽  
Catalytic Combustion Of Methane
Sign in / Sign up

Export Citation Format

Share Document