Maximum likelihood estimation of transformer high frequency parameters from test data

1991 ◽  
Vol 6 (2) ◽  
pp. 858-865 ◽  
Author(s):  
A. Keyhani ◽  
S.W. Chua ◽  
S.A. Sebo
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Test Data ◽  
High Frequency ◽  
Likelihood Estimation

scholarly journals Estimation of Weibull parameters using artificial neural network

2021 ◽  
Author(s):  
Md. Sujauddin Mallick
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Test Data ◽  
Mathematical Formulation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
Data Set

Weibull distribution is an important distribution in the field of reliability. In this distribution usually there are two parameters. The usual parameter estimation method is maximum likelihood estimation. Maximum likelihood estimation requires mathematical formulation and prior assumption. Non parametric method such as neural network does not require prior assumption and mathematical formulation. They need data to formulate the model. In this report feed forward neural network with back propagation is used to estimate the parameters of a two-parameter Weibull distribution based on four Scenarios. The Scenario consists of training and test data set. Training and test data set generated through simulated time to failure events using wblrnd function in MATLAB. The input to the network is time to failure, and the output is shape and scale parameters. The network is trained and tested using trainbr algorithm in MATLAB. The network performed better on Scenario 2 which has the larger number of training examples of shape and scale.

A Comparison of Two Methods of Decomposing Item Difficulties

1987 ◽  
Vol 12 (4) ◽  
pp. 369-381 ◽  
Author(s):  
Kathy E. Green ◽  
Richard M. Smith
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Sample Size ◽  
Test Data ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Test Model ◽  
Estimation Of Parameters ◽  
Linear Logistic Test Model ◽  
Conditional Maximum

This paper compares two methods of estimating component difficulties for dichotomous test data. Simulated data are used to study the effects of sample size, collinearity, a measurement disturbance, and multidimensionality on the estimation of component difficulties. The two methods of estimation used in this study were conditional maximum likelihood estimation of parameters specified by the linear logistic test model (LLTM) and estimated Rasch item difficulties regressed on component frequencies. The results of the analysis indicate that both methods produce similar results in all comparisons. Neither of the methods worked well in the presence of an incorrectly specified structure or collinearity in the component frequencies. However, both methods appear to be fairly robust in the presence of measurement disturbances as long as there is a large number of cases (n = 1,000). For the case of fitting data with uncorrelated component frequencies, 30 cases were sufficient to recover the generating parameters accurately.

Full information from measured ADC test data using maximum likelihood estimation

Measurement ◽  
2012 ◽  
Vol 45 (2) ◽  
pp. 164-169 ◽  
Author(s):  
László Balogh ◽  
István Kollár ◽  
Linus Michaeli ◽  
Ján Šaliga ◽  
Jozef Lipták
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Test Data ◽  
Likelihood Estimation ◽  
Full Information ◽  
Adc Test
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