On the frequency distribution of earthquake magnitude and intensity

1980 ◽  
Vol 70 (6) ◽  
pp. 2253-2260 ◽  
Author(s):  
Berislav Makjanić
Keyword(s):  
Linear Regression ◽  
Exponential Distribution ◽  
Frequency Distribution ◽  
Initial Distribution ◽  
Earthquake Magnitude ◽  
Generalized Exponential Distribution ◽  
Macroseismic Intensities ◽  
The Given ◽  
New Distribution

abstract Since the usual Gutenberg and Richter distribution of magnitudes does not give good fit to the observed data except in the middle of the range, it is proposed to find a new distribution of magnitudes starting from the two principles: (a) distribution of all magnitudes (initial distribution) has to be limited; and (b) distribution of the largest values (extremal distribution) has to be asymptotic extremal distribution for the given initial distribution. Since it is well established that the largest values of intensity and magnitude follow the so-called third asymptote, which is conveniently written in Jenkinson's form, the initial distribution which belongs to this extremal distribution is determined. A procedure for determining the parameters of the initial distribution is proposed. For the time being, better results are obtained from macroseismic intensities, since often the magnitudes are being determined from these intensities by means of the linear regression. As an example, the frequency distribution of the 100-yr series of intensity of Zagreb earthquakes is analyzed. It seems that this initial distribution, which turns out to be the generalized exponential distribution, gives good fit to the data.

scholarly journals Modeling Life Time Data by Generalized Weibull-generalized Exponential Distribution

2020 ◽  
pp. 65-75
Author(s):  
N. I. Badmus ◽  
Faweya, Olanrewaju
Keyword(s):  
Exponential Distribution ◽  
Information Matrix ◽  
Real Life ◽  
Likelihood Estimation ◽  
Life Time ◽  
Generalized Exponential Distribution ◽  
Time Data ◽  
Air Conditioning System ◽  
Generalized Distributions ◽  
New Distribution

This paper convolutes two generalized distributions from the family of generated T - X distribution. The new distribution generated from these distributions is called the Generalized Weibull-generalized Exponential Distribution. The properties of the proposed distribution are derived. Method of maximum likelihood estimation is used to estimate the parameters of the distribution and the information matrix is obtained. Thereafter, the distribution is applied to a real life dataset of failure for the air conditioning system and the obtained results are compared with other existing distributions to illustrate the capability and flexibility of the new distribution.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Univariate and bivariate extensions of the generalized exponential distributions

2021 ◽  
Vol 71 (6) ◽  
pp. 1581-1598
Author(s):  
Vahid Nekoukhou ◽  
Ashkan Khalifeh ◽  
Hamid Bidram
Keyword(s):  
Em Algorithm ◽  
Exponential Distribution ◽  
Bivariate Distribution ◽  
Generalized Exponential Distribution ◽  
Exponential Distributions ◽  
Unknown Parameters ◽  
Data Set ◽  
New Class ◽  
Special Cases ◽  
Univariate Distribution

Abstract The main aim of this paper is to introduce a new class of continuous generalized exponential distributions, both for the univariate and bivariate cases. This new class of distributions contains some newly developed distributions as special cases, such as the univariate and also bivariate geometric generalized exponential distribution and the exponential-discrete generalized exponential distribution. Several properties of the proposed univariate and bivariate distributions, and their physical interpretations, are investigated. The univariate distribution has four parameters, whereas the bivariate distribution has five parameters. We propose to use an EM algorithm to estimate the unknown parameters. According to extensive simulation studies, we see that the effectiveness of the proposed algorithm, and the performance is quite satisfactory. A bivariate data set is analyzed and it is observed that the proposed models and the EM algorithm work quite well in practice.

Absolute Continuous Multivariate Generalized Exponential Distribution

Sankhya B ◽  
2015 ◽  
Vol 77 (2) ◽  
pp. 175-206 ◽  
Author(s):  
Debasis Kundu ◽  
Ankush Kumar ◽  
Arjun K. Gupta
Keyword(s):  
Exponential Distribution ◽  
Generalized Exponential Distribution

Exponentiated Generalized Exponential Distribution: Ordinary Differential Equations

2018 ◽  
pp. 341-352
Author(s):  
Hilary I. Okagbue ◽  
Pelumi E. Oguntunde ◽  
Paulinus O. Ugwoke ◽  
Abiodun A. Opanuga ◽  
Ezinne C. Erondu
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Exponential Distribution ◽  
Generalized Exponential Distribution

scholarly journals Linear Regression Algorithm in Machine Learning through MATLAB

2021 ◽  
Vol 9 (12) ◽  
pp. 989-995
Author(s):  
Kalva Sindhu Priya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Critical Part ◽  
Squared Error ◽  
Master Level ◽  
Predicted Model ◽  
The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

scholarly journals The Weibull Generalized Exponential Distribution with Censored Sample: Estimation and Application on Real Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hisham M. Almongy ◽  
Ehab M. Almetwally ◽  
Randa Alharbi ◽  
Dalia Alnagar ◽  
E. H. Hafez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Exponential Distribution ◽  
Estimation Method ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Real Data ◽  
Mcmc Methods ◽  
Generalized Exponential Distribution ◽  
Censored Sample ◽  
Sample Maximum

This paper is concerned with the estimation of the Weibull generalized exponential distribution (WGED) parameters based on the adaptive Type-II progressive (ATIIP) censored sample. Maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation based on Markov chain Monte Carlo (MCMC) methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP-censored sample, and also, we made a bootstrap confidence interval estimation. We will analyze data related to the distribution about single carbon fiber and electrical data as real data cases to show how the schemes work in practice.

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