Identification of the effective distribution function for determination of the distributed activation energy models using the maximum likelihood method: Isothermal thermogravimetric data

2009 ◽  
Vol 41 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Bojan Janković
Keyword(s):  
Activation Energy ◽  
Distribution Function ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Energy Models ◽  
Thermogravimetric Data ◽  
Effective Distribution

scholarly journals A New Approach to Assess Wind Potential

2021 ◽  
Keyword(s):  
Distribution Function ◽  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Wind Power ◽  
Cumulative Distribution Function ◽  
Maximum Likelihood Method ◽  
New Method ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Wind Distribution

<p>Weibull Cumulative Distribution Function (C.D.F.) has been employed to assess and compare wind potentials of two wind stations Europlatform and Stavenisse of The Netherland. Weibull distribution has been used for accurate estimation of wind energy potential for a long time. The Weibull distribution with two parameters is suitable for modeling wind data if wind distribution is unimodal. Whereas wind distribution is generally unimodal, random weather changes can make the distribution bimodal. It is always desirable to find a method that accurately represents actual statistical data. Some well-known statistical methods are Method of Moment (MoM), Linear Least Square Method (LLSM), Maximum Likelihood Method (M.L.M.), Modified Maximum Likelihood Method (MMLM), Energy Pattern Factor Method (EPFM), and Empirical Method (E.M.), etc. All these methods employ Probability Density Function (PDF) of Weibull distribution, except LLSM, which uses Cumulative Distribution Function (C.D.F.). In this communication, we are presenting a newly proposed method of evaluating Weibull parameters. Unlike most methods, this new method employs a cumulative distribution function. A MATLAB® GUI-based simulation is developed to estimate Weibull parameters using the C.D.F. approach. It is found that the Mean Square Error (M.S.E.) is the lowest when using the new method. The new method, therefore, estimates wind power density with reasonable accuracy. Wind Power (W.P.) is estimated by considering four different Wind Turbine (W.T.) models for two sites, and maximum W.P. is found using Evance R9000.</p>

Discussion on the maximum likelihood method for determination of membership in open clusters

1982 ◽  
Vol 6 (4) ◽  
pp. 293-298 ◽  
Author(s):  
Zhao Jun-liang ◽  
Tian Kai-ping ◽  
Xu Zong-hai ◽  
Yin Ming-guan
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Open Clusters ◽  
Likelihood Method

scholarly journals Lomax Weibull Distribution

2019 ◽  
pp. 1-18 ◽  
Author(s):  
Benjamin Apam ◽  
Nasiru Suleman ◽  
Emmanuel Adjei
Keyword(s):  
Distribution Function ◽  
Maximum Likelihood ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Maximum Likelihood Method ◽  
Moment Generating Function ◽  
Likelihood Method ◽  
Survival Functions ◽  
Score Functions ◽  
The Moment

In this article, we introduce the Lomax-Weibull (LoW) distribution using the method of composition of CDFs from the Lomax and Weibull distributions. Expressions for the moment generating function, hazard and survival functions were derived. A plot of the probability distribution function and cumulative distributions were done using the Python software. We also used the maximum likelihood method of estimation to derive the score functions for estimating the parameters of the distribution.

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