Modified Maximum-Likelihood Method for Non-Normal Time Series Revisited

2004 ◽  
Vol 33 (2) ◽  
pp. 397-417 ◽  
Author(s):  
Taylan A. Ula ◽  
Ceylan Yozgatligil#
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Modified Maximum Likelihood

scholarly journals Estimation of P(Y < X) in a Four-Parameter Generalized Gamma Distribution

2016 ◽  
Vol 41 (3) ◽  
Author(s):  
M. Masoom Ali ◽  
Manisha Pal ◽  
Jungsoo Woo
Keyword(s):  
Maximum Likelihood ◽  
Closed Form ◽  
Importance Sampling ◽  
Simulation Study ◽  
Maximum Likelihood Method ◽  
Sampling Procedure ◽  
Likelihood Method ◽  
Scale Parameters ◽  
Generalized Gamma ◽  
Modified Maximum Likelihood

In this paper we consider estimation of R = P(Y < X), when X and Y are distributed as two independent four-parameter generalized gamma random variables with same location and scale parameters. A modified maximum likelihood method and a Bayesian technique have been used to estimate R on the basis of independent samples. As the Bayes estimator cannot be obtained in a closed form, it has been implemented using importance sampling procedure. A simulation study has also been carried out to compare the two methods.

scholarly journals Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

2021 ◽  
Vol 12 (2) ◽  
pp. 1114
Author(s):  
Ahmed Samir Badawi ◽  
Siti Hajar Yusoff ◽  
Alhareth Mohammed Zyoud ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Numerical Methods ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Prediction Accuracy ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Mean Square ◽  
Modified Maximum Likelihood

This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘<em>k</em>’ and the Weibull scale factor ‘<em>c</em>’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X<sup>2</sup>), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.

A DIFFUSION APPROACH TO ECONOMIC TIME SERIES

2000 ◽  
Vol 03 (03) ◽  
pp. 567-568
Author(s):  
M. CIOGLI ◽  
G. ROTUNDO ◽  
B. TIROZZI
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Diffusion Equation ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Economic Time Series ◽  
Martingale Theory ◽  
Hedging Strategy ◽  
Economic Time

A diffusion equation for the price evolution of the Italian share "Olivetti" is found by investigating a series of its data. The coefficients of this equation are found by using the maximum likelihood method based on martingale theory. We evaluate pricing and hedging strategy by the Sornette and Bouchaud approach.

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