scholarly journals B40 Group Income Household Trend in Malaysia

2020 ◽  
Vol 9 (12) ◽  
pp. 126-129
Keyword(s):  
Household Income ◽  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
Complex Model ◽  
Parameter Distribution ◽  
Average Income ◽  
Goodness Of Fit Test ◽  
Income Household ◽  
The Future ◽  
Income Data

Income inequality is crucial issue in the Malaysian economy. This issue has a great impact especially on the B40 group income household because of the rising cost of living today. Therefore, modelling of income data is done to look at income pattern of B40 group in Malaysia. Household income data for Malaysia in year 2007, 2009, 2012, 2014 and 2016 have been used in this study. The income distribution used in this study is a two-parameter distribution of Weibull, Log Normal, Fisk and Gamma. This study uses only two parametric distributions to suit the income data because the simplest model is better than the complex model. The best distribution selection is performed with the fitting of statistical distribution through maximum likelihood estimation (MLE) method. Goodness of fit test has been done to model B40 household income data. The best model for each year used to predict the average income in the future by using regression method. Weibull distribution is the best model for B40 household income data. The study also shows that the average income of the B40 group in the future will increase. Therefore, this study was conducted to assist B40 group to be more sensitive to the Malaysian economy and plan their income wisely.

scholarly journals Composite Pareto Distributions for Modelling Household Income Distribution in Malaysia

2021 ◽  
Vol 50 (7) ◽  
pp. 2047-2058
Author(s):  
Muhammad Hilmi Abdul Majid ◽  
Kamarulzaman Ibrahim
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Goodness Of Fit ◽  
Threshold Value ◽  
Goodness Of Fit Test ◽  
Income Distributions ◽  
Pareto Distributions ◽  
Tail Behaviour ◽  
Kolmogorov Smirnov ◽  
Income Data

Composite Pareto distributions are flexible as the models allow for data to be described by two distributions: a Pareto distribution for the data above a threshold value and another separate distribution for data below the threshold value. It is noted in some previous literatures that the Paretian tail behaviour can be observed in the distribution of Malaysian household income. In this paper, the composite Pareto models are fitted to the Malaysian household income data of several years. These fitted composite Pareto models are then compared to several univariate models for describing income distribution using pseudo-likelihood based AIC, BIC and Kolmogorov-Smirnov goodness-of-fit test. It is found that the income distributions in Malaysia can be best described by the lognormal-Pareto (II) model as compared to other candidate models.

scholarly journals Modelling the number of olive groves in Spanish municipalities

2016 ◽  
Vol 14 (1) ◽  
pp. e0201
Author(s):  
Maria-Dolores Huete ◽  
Juan A. Marmolejo
Keyword(s):  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
External Factors ◽  
Discrete Distributions ◽  
Future Research ◽  
Total Output ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Total Variability ◽  
Olive Groves

<p>The univariate generalized Waring distribution (UGWD) is presented as a new model to describe the goodness of fit, applicable in the context of agriculture. In this paper, it was used to model the number of olive groves recorded in Spain in the 8,091 municipalities recorded in the 2009 Agricultural Census, according to which the production of oil olives accounted for 94% of total output, while that of table olives represented 6% (with an average of 44.84 and 4.06 holdings per Spanish municipality, respectively). UGWD is suitable for fitting this type of discrete data, with strong left-sided asymmetry. This novel use of UGWD can provide the foundation for future research in agriculture, with the advantage over other discrete distributions that enables the analyst to split the variance. After defining the distribution, we analysed various methods for fitting the parameters associated with it, namely estimation by maximum likelihood, estimation by the method of moments and a variant of the latter, estimation by the method of frequencies and moments. For oil olives, the chi-square goodness of fit test gives <em>p</em>-values of 0.9992, 0.9967 and 0.9977, respectively. However, a poor fit was obtained for the table olive distribution. Finally, the variance was split, following Irwin, into three components related to random factors, external factors and internal differences. For the distribution of the number of olive grove holdings, this splitting showed that random and external factors only account about 0.22% and 0.05%. Therefore, internal differences within municipalities play an important role in determining total variability.</p>

scholarly journals Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Abdisalam Hassan Muse ◽  
Ahlam H. Tolba ◽  
Eman Fayad ◽  
Ola A. Abu Ali ◽  
M. Nagy ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
Cumulative Distribution ◽  
Logistic Distribution ◽  
Parameter Distribution ◽  
Unknown Parameters ◽  
Failure Rate Function ◽  
Competing Models ◽  
Two Parameter

The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall–Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.

scholarly journals A Modified Chi-square Type Test Statistic for the Double Burr X Model with Applications to Right Censored Medical and Reliability Data

2021 ◽  
pp. 615-623
Author(s):  
Khaoula Aidi ◽  
Nadeem Shafique Butt ◽  
Mir Masoom Ali ◽  
Mohamed Ibrahim ◽  
Haitham M. Yousof ◽  
...  
Keyword(s):  
Censored Data ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Right Censored Data ◽  
The Right

A new modified version of the Bagdonavičius-Nikulin goodness-of-fit test statistic is presented for validity for the right censor case under the double Burr type X distribution. The maximum likelihood estimation method in censored data case is used and applied. Simulations via the algorithm of Barzilai-Borwein is performed for assessing the right censored estimation method. Another simulation study is presented for testing the null hypothesis under the modified version of the Bagdonavičius and Nikulin goodness-of-fit statistical test. Four right censored data sets are analyzed under the new modified test statistic for checking the distributional validation.

scholarly journals Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Minh H. Pham ◽  
Chris Tsokos ◽  
Bong-Jin Choi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Censored Data ◽  
Goodness Of Fit ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Likelihood Estimation ◽  
R Package ◽  
Goodness Of Fit Test ◽  
Generalized Pareto

The generalized Pareto distribution (GPD) is a flexible parametric model commonly used in financial modeling. Maximum likelihood estimation (MLE) of the GPD was proposed by Grimshaw (1993). Maximum likelihood estimation of the GPD for censored data is developed, and a goodness-of-fit test is constructed to verify an MLE algorithm in R and to support the model-validation step. The algorithms were composed in R. Grimshaw’s algorithm outperforms functions available in the R package ‘gPdtest’. A simulation study showed the MLE method for censored data and the goodness-of-fit test are both reliable.

Research on Complex Equipment Reliability Growth AMSAA-ELP Model Based on Explore Learning Promotion

2014 ◽  
Vol 940 ◽  
pp. 531-534 ◽  
Author(s):  
Na Zhang
Keyword(s):  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
Trend Test ◽  
Reliability Growth ◽  
Goodness Of Fit Test ◽  
Model Based ◽  
Estimation Formula ◽  
Equipment Reliability ◽  
Complex Equipment

According to learning other models equipment test information of complex equipment in the development process, and making their own systems to improve the reliability of the case, a complex equipment reliability growth AMSAA-ELP model based on explore learning promotion was developed, and the property was analyzed from different parameter values. Additionally, the trend test was also presented. Secondly, maximum likelihood estimation formula of the parameters was given under time censored and failure censored test of AMSAA-ELP model, and point out that there are multiple poles value of the maximum likelihood estimate can use pseudo-Monte-Carlo method parameter calculation. Additionally, the model's goodness of fit test was also given. Finally, the combination of complex equipment with engine failure data was analyzed. The results shows that the AMSAA-ELP model is prefer to AMSAA model intended to test data, and the AMSAA-ELP model is suitable to the engineering applications.

scholarly journals Maximum Likelihood Estimation for the Generalized Pareto Distribution and Goodness-of-Fit Test with Censored Data

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Maddalena Cavicchioli ◽  
Angeliki Papana ◽  
Ariadni Papana Dagiasis ◽  
Barbara Pistoresi
Keyword(s):  
Goodness Of Fit ◽  
Pareto Distribution ◽  
A Priori ◽  
Generalized Pareto Distribution ◽  
Likelihood Estimation ◽  
Large Database ◽  
Goodness Of Fit Test ◽  
Institutional Variables ◽  
Generalized Pareto ◽  
Selection Of

A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.

Research on K Distribution in Anderson-Darling Goodness-of-Fit Test

2013 ◽  
Vol 475-476 ◽  
pp. 27-31
Author(s):  
Rui Feng ◽  
Yun Xue Liu ◽  
Wen Qiang Fan
Keyword(s):  
Goodness Of Fit ◽  
Small Sample Size ◽  
Critical Value ◽  
Complex Model ◽  
Small Sample ◽  
Radar Signal ◽  
Test Results ◽  
Goodness Of Fit Test ◽  
Radar Clutter ◽  
Anderson Darling

Identification of radar clutter model is one important step to choose optimum CFAR processor in radar signal detection, and Anderson-Darling (AD) goodness-of-fit test considered the practical and effective way under small sample size. As K distribution is a complex model providing suitable description for the amplitude of microwave sea clutter, the research on K distribution in AD test is useful but very difficult. Moment-based methods are widely used to estimate parameters of K distribution and comparison show that methods with lower moments are more accurate. Further in AD test, the influence of shape parameter on critical value (CV) for K distribution is presented. Taking it into account, tables of AD test results using varied critical value advanced in our study and fixed critical value proposed in previous research are shown based on Monte Carlo simulations.

scholarly journals An Analytical EM Algorithm for Sub-Gaussian Vectors

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 945
Author(s):  
Audrius Kabašinskas ◽  
Leonidas Sakalauskas ◽  
Ingrida Vaičiulytė
Keyword(s):  
Em Algorithm ◽  
Likelihood Ratio ◽  
Goodness Of Fit ◽  
Stable Distribution ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Normal Vector ◽  
Ratio Test ◽  
Estimation Of Parameters ◽  
Goodness Of Fit Test

The area in which a multivariate α-stable distribution could be applied is vast; however, a lack of parameter estimation methods and theoretical limitations diminish its potential. Traditionally, the maximum likelihood estimation of parameters has been considered using a representation of the multivariate stable vector through a multivariate normal vector and an α-stable subordinator. This paper introduces an analytical expectation maximization (EM) algorithm for the estimation of parameters of symmetric multivariate α-stable random variables. Our numerical results show that the convergence of the proposed algorithm is much faster than that of existing algorithms. Moreover, the likelihood ratio (goodness-of-fit) test for a multivariate α-stable distribution was implemented. Empirical examples with simulated and real world (stocks, AIS and cryptocurrencies) data showed that the likelihood ratio test can be useful for assessing goodness-of-fit.

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