Studying psychotherapy change in narrative terms: The innovative moments method

2020 ◽  
Vol 20 (3) ◽  
pp. 442-448 ◽  
Author(s):  
João Batista ◽  
Joana Silva ◽  
Carina Magalhães ◽  
Helena Ferreira ◽  
Pablo Fernández‐Navarro ◽  
...  
Keyword(s):  
Moments Method ◽  
Innovative Moments

scholarly journals Moments Method for Shell-Model Level Density

2016 ◽  
Vol 665 ◽  
pp. 012048 ◽  
Author(s):  
V Zelevinsky ◽  
M Horoi ◽  
R A Sen'kov
Keyword(s):  
Shell Model ◽  
Level Density ◽  
Moments Method

scholarly journals Generalized Modified Slash Birnbaum–Saunders Distribution

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 724 ◽  
Author(s):  
Jimmy Reyes ◽  
Inmaculada Barranco-Chamorro ◽  
Diego Gallardo ◽  
Héctor Gómez
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Standard Normal Distribution ◽  
Stochastic Representation ◽  
Moments Method ◽  
Standard Normal ◽  
Newton Raphson ◽  
New Distribution ◽  
Skewness And Kurtosis ◽  
Modified Moments

In this paper, a generalization of the modified slash Birnbaum–Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on modified moments method and maximum likelihood (ML). To obtain ML estimates, two approaches are considered: Newton–Raphson and EM-algorithm. Applications reveal that it has potential for doing well in real problems.

scholarly journals Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Héctor J. Gómez ◽  
Diego I. Gallardo ◽  
Karol I. Santoro
Keyword(s):  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
R Software ◽  
Data Application ◽  
The Em Algorithm ◽  
Kurtosis Parameter ◽  
Computational Implementation ◽  
Moments Method

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

scholarly journals Optimization method based on minimization m-Order central moments used in surveying engineering problems

2021 ◽  
Author(s):  
Sławomir Cellmer
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Optimization Method ◽  
Railway Track ◽  
Computational Techniques ◽  
Central Moments ◽  
Numerical Tests ◽  
Moments Method ◽  
Engineering Problems ◽  
Set Of Points

A new optimization method presented in this work – the Least m-Order Central Moments method, is a generalization of the Least Squares method. It allows fitting a geometric object into a set of points in such a way that the maximum shift between the object and the points after fitting is smaller than in the Least Squares method. This property can be very useful in some engineering tasks, e.g. in the realignment of a railway track or gantry rails. The theoretical properties of the proposed optimization method are analyzed. The computational problems are discussed. The appropriate computational techniques are proposed to overcome these problems. The detailed computational algorithm and formulas of iterative processes have been derived. The numerical tests are presented, in order to illustrate the operation of proposed techniques. The results have been analyzed, and the conclusions were then formulated.

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