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Characterizations of Marshall-Olkin Discrete Reduced Modified Weibull Distribution
Gholamhossein G. Hamedani
Issue:
Volume 5, Issue 1, March 2019
Pages:
1-4
Received:
29 August 2018
Accepted:
22 April 2019
Published:
20 May 2019
Abstract: Characterizing a distribution is an important problem in applied sciences, where an investigator is vitally interested to know if their model follows the right distribution. To this end, the investigator relies on conditions under which their model would follow specifically chosen distribution. Certain characterizations of the Marshall-Olkin discrete reduced modified Weibull distribution are presented to complete, in some way, their work.
Abstract: Characterizing a distribution is an important problem in applied sciences, where an investigator is vitally interested to know if their model follows the right distribution. To this end, the investigator relies on conditions under which their model would follow specifically chosen distribution. Certain characterizations of the Marshall-Olkin discre...
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Estimation of Response Propensities Using the Previous Survey
Issue:
Volume 5, Issue 1, March 2019
Pages:
5-9
Received:
13 April 2019
Accepted:
10 May 2019
Published:
4 June 2019
Abstract: Many surveys are carried out yearly, and the implementation of the surveys remains the same from year to year. Experience from a previous survey is useful when planning a new survey, because the response behavior usually remains quite the same in subsequent years. This paper studies how response propensities, estimated using the dataset of the previous survey, predict actual response rates. In this study, two consecutive datasets of the European Social Survey were available. The both datasets contained same register variables. Response propensities were estimated to the older dataset using a logistic regression model. Then the propensities were imputed to the newer dataset using a donor-recipient method. The imputation was based on the explanatory variables of the logistic regression model so that the donor and the recipient had the same values in the variables. Then it was examined if there was a connection between the imputed response propensities and actual response rates. The result was that the imputed response propensities predicted the response behavior quite well. People with low response propensities were often nonrespondents, and people with high response propensities were often respondents. Using the previous survey, it is possible to calculate response propensities for a new sample before the data collection of the survey has been started. Then challenging respondents are known before the data collection, and this information is useful for data collection.
Abstract: Many surveys are carried out yearly, and the implementation of the surveys remains the same from year to year. Experience from a previous survey is useful when planning a new survey, because the response behavior usually remains quite the same in subsequent years. This paper studies how response propensities, estimated using the dataset of the prev...
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Fitting the Ehrenberg’s Law-Like Equation Using the Age, Weight and Height of Secondary School Students
Nwagwu Chibuikem Chrysogonus,
Nduka Ethelbert Chinaka,
Ogoke Uchenna Petronilla
Issue:
Volume 5, Issue 1, March 2019
Pages:
10-14
Received:
4 April 2019
Accepted:
21 May 2019
Published:
5 June 2019
Abstract: The Ehrenberg’s law like equation is deemed to fit (within minor error) universally among children aged 11 – 17. This paper adopts this equation to confirm or deny the universal applicability based on selected schools in South-South region in Nigeria which is believed to be environmentally degraded. Data on Age, Sex and anthropometric measures (weight and height) of the cohort in public and private schools were used to fit the model. After the data were pooled from the public and private school, Microsoft Excel Package was used to test how different conditions may influence the fit of the model to the data. The conditions considered were: Gender and Social class (determined by school type), hence there were five different data classifications. Results obtained showed that although the average deviation recorded for female students fell out of the expected limit of ±0.01 although marginally by another +0.01; all other subsets of the data set were found to fit the given equation appropriately. This goes to show that irrespective of gender or social class, provided the students are within the specified age bracket, the Ehrenberg's law like equation will be a good model for use. This is equally verified using the test of significance as well as the test for equality of the regression models.
Abstract: The Ehrenberg’s law like equation is deemed to fit (within minor error) universally among children aged 11 – 17. This paper adopts this equation to confirm or deny the universal applicability based on selected schools in South-South region in Nigeria which is believed to be environmentally degraded. Data on Age, Sex and anthropometric measures (wei...
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Spatial Cumulative Probit Model: An Application to Poverty Classification and Mapping
Issue:
Volume 5, Issue 1, March 2019
Pages:
15-21
Received:
12 October 2018
Accepted:
7 November 2018
Published:
11 June 2019
Abstract: Previous studies on household poverty classification have commonly dichotomized the dependent variable into non-poor or poor, and used binary models. This way, the most extreme categories of poverty, which are usually the main targets of interventions, are not identified. Moreover, expenditure data used to describe poverty is typically collected at several locations over large geographical domains. Local disturbances introduce spatial correlation, implying that global parameters (obtained via independence assumptions of standard statistical methods) cannot adequately describe site-specific conditions of the data. The objective, therefore, is to describe an appropriate method for ordered categorical data collected at geo-referenced locations over large geographical space. To achieve this, a model named Spatial Cumulative Probit Model (SCPM) was proposed. This model classified household poverty in an ordinal spatial framework. Bayesian inference was performed on data sampled by Markov Chain Monte Carlo (MCMC) algorithms. A test of model adequacy show that the SCPM is unbiased and attains a lower misclassification rate of 14.43% than the simple Cumulative Probit (CP) model with misclassification rate of 16.5% that ignores spatial dependence in the data. Overall, ‘savannah ecological zone’, ‘polygamous marriage’ and ‘rural location’ were the most powerful predictors of extreme poverty in Ghana. The prediction map, created by this study, identified positive correlation with respect to ‘poor’ and ‘extremely poor’ categories. Results of the model in this study can be considered a category and site-specific report that identifies all levels and sites of poverty for easy targeting, thus, avoiding the blanket approach that prefers the one-fits-it-all solution to the problem of poverty. Analysis was based on the Ghana Living Standards Survey (GLSS 6) dataset.
Abstract: Previous studies on household poverty classification have commonly dichotomized the dependent variable into non-poor or poor, and used binary models. This way, the most extreme categories of poverty, which are usually the main targets of interventions, are not identified. Moreover, expenditure data used to describe poverty is typically collected at...
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