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Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India
M. Sundar Rajan,
M. Palanivel
Issue:
Volume 4, Issue 1, March 2018
Pages:
1-5
Received:
11 September 2017
Accepted:
11 November 2017
Published:
19 January 2018
Abstract: Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.
Abstract: Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, comp...
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A New Class of Generalized Burr III Distribution for Lifetime Data
Olobatuyi Kehinde,
Asiribo Osebi,
Dawodu Ganiyu
Issue:
Volume 4, Issue 1, March 2018
Pages:
6-21
Received:
4 January 2018
Accepted:
24 February 2018
Published:
28 March 2018
Abstract: For the first time, the Generalized Gamma Burr III (GGBIII) is introduced as an important model for problems in several areas such as actuarial sciences, meteorology, economics, finance, environmental studies, reliability, and censored data in survival analysis. A review of some existing gamma families have been presented. It was found that the distributions cannot exhibit complicated shapes such as unimodal and modified unimodal shapes which are very common in medical field. The Generalized Gamma Burr III (GGBIII) distribution which includes the family of Zografos and Balakrishnan as special cases is proposed and studied. It is expressed as the linear combination of Burr III distribution and it has a tractable properties. Some mathematical properties of the new distribution including hazard, survival, reverse hazard rate function, moments, moments generating function, mean and median deviations, distribution of the order statistics are presented. Maximum likelihood estimation technique is used to estimate the model parameters and applications to real datasets in order to illustrate the usefulness of the model are presented. Examples and applications as well as comparisons of the GGBIII to the existing Gamma-G families are given.
Abstract: For the first time, the Generalized Gamma Burr III (GGBIII) is introduced as an important model for problems in several areas such as actuarial sciences, meteorology, economics, finance, environmental studies, reliability, and censored data in survival analysis. A review of some existing gamma families have been presented. It was found that the dis...
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Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining
Xiao Yang,
Caiyun Ying,
Yefeng Zhou
Issue:
Volume 4, Issue 1, March 2018
Pages:
22-28
Received:
26 April 2018
Published:
27 April 2018
Abstract: Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.
Abstract: Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data min...
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Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan
Pauline Nyathira Kamau,
Lucy Muthoni,
Collins Odhiambo
Issue:
Volume 4, Issue 1, March 2018
Pages:
29-37
Received:
13 June 2018
Accepted:
17 July 2018
Published:
13 August 2018
Abstract: In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.
Abstract: In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter...
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