scholarly journals Factors determining the operational self-sufficiency of microfinance institutions

2021 ◽  
Vol 7 (2) ◽  
pp. 1-13
Author(s):  
Nejra Hadžiahmetović
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Social Goals ◽  
Microfinance Institutions ◽  
The Public ◽  
Independent Variables ◽  
Self Sufficiency ◽  
The World ◽  
Widespread Belief ◽  
Core Mission

Abstract The main aim of this paper is to explore the factors determining Microfinance institutions (MFIs) self-sufficiency. The data on selected variables for this research were obtained from the public MIX Market Database and cover the year of 2017. The empirical model is constructed with application of a Principal Component Analysis (PCA) and Logistic regression analysis. Sample is consisted of 342 MFIs from all around the world, with 21 independent variables grouped into eight factors/components, and OSS (operational self-sufficiency) as dependent variable. The obtained results suggest that higher revenue and MFIs profitability combined with decrease of credit risk lead to higher probability of MFI to be self-sufficient. These results also confirm widespread belief that MFIs will not be able to achieve their social goals without achieving sustainable profitability. In addition, results also confirm importance of MFIs core mission as with increase in outreach, probability of MFIs achieving self-sustainability also increases.

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

scholarly journals What Do Global Metrics Tell Us about the World?

2019 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Global Indicators ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global indicators that measure different economic, political and social attributes of countries in the world. This paper seeks to answer two questions. First, what is the degree of overlap between these different measures? Are they, in fact, measuring the same underlying dimension? To answer this question, we employ a principal component analysis (PCA) to 15 indices across 145 countries. The results demonstrate that there is one underlying dimension that combines economic development and social progress with state stability. Second, how do countries score on this dimension? The results of the PCA allow us to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as rich, poor and middle countries. A five-group classification provided a more nuanced categorization described as: The very rich, free and stable; affluent and free; upper middle; lower middle; poor and not free.

scholarly journals Financial Stability Indicators – The Case of Croatia

2016 ◽  
Vol 5 (1) ◽  
pp. 113-140 ◽  
Author(s):  
Mirna Dumičić
Keyword(s):  
Principal Component Analysis ◽  
Systemic Risk ◽  
Financial Stability ◽  
Principal Component ◽  
Small Open Economies ◽  
Macroprudential Policy ◽  
Policy Makers ◽  
The Public ◽  
Systemic Risks ◽  
Accumulation Index

Abstract This paper considers financial stability through the processes of accumulation and materialisation of systemic risks. To this end, the method of principal component analysis on the example of Croatia has been used to construct two composite indicators – a systemic risk accumulation index and an index reflecting the consequences of systemic risk materialisation. In the construction of the indices, the features and risks specific to small open economies were considered. Such an approach to systemic risk analysis facilitates the monitoring and understanding of the degree of financial stability and communication of macroprudential policy makers with the public.

scholarly journals Harnessing the Predictive Power of Lower-division Statistics of Cricketers to Predict Their Rates of Success at the International Level

2021 ◽  
Vol 9 (12) ◽  
pp. 489-499
Author(s):  
Vishal C V
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Dimensionality Reduction ◽  
Predictive Power ◽  
Principal Component ◽  
International Level ◽  
Sports Analytics ◽  
The World ◽  
Lower Division ◽  
Different Parts

Abstract: Statistics has always been an integral part of the sporting world. Selectors pick players based on numerous factors such as averages, strike-rates, runs scored or goals scored. Teams have exclusive ‘talent hunters’, who spend weeks, if not months, trying to uncover talent from different parts of the world. With the rise of this new niche field called Sports Analytics, teams can now perform player evaluations on tons of data that is available. This paper aims to examine the factors that truly indicate the capacity of cricket players to perform at the top-most level – international cricket. Though this research has been carried out on cricket data, it is hoped that similar methods can be used to hunt for true talent in other sports! Keywords: Cricket Analytics, Random Forest, Principal Component Analysis, Dimensionality Reduction.

scholarly journals Attitude Towards Zakah on Employment Income : Comparing Outcomes Between Single Score and Multidimensional Scores

2020 ◽  
Author(s):  
Kamil Md Idris ◽  
Ahmad Mahdzan Ayob
Keyword(s):  
Principal Component Analysis ◽  
Goodness Of Fit ◽  
Principal Component ◽  
Complex Object ◽  
Factor Scores ◽  
Multivariate Logistic Regression ◽  
Independent Variables ◽  
Compliance Behavior ◽  
Single Score ◽  
The Relationship

Study on attitude towards regulated social activities have been carried out in many areas (such as tax and zakah payment). However, many of these studies applied a single score of attitude in their analyses. Such a procedure, to some researchers is considered less informative, especially in the study of a complex attitude which has several dimensions. Many researchers have suggested that attitude towards a complex object should be studied by decomposing the object or issue into smaller and less complex elements on the basis of component parts, specific functions, or particular contexts. Thus, this paper offers a comparative study of outcomes between attitude measured by a single summative score and attitude measured by multidimensional factor scores. The object of attitude in this paper is zakah on employment income by eligible Muslim. In the first approach, a total of 24 items of attitude were used to represent the single score of attitude. In the second approach, principal component analysis with varimax ratation was first applied to determine the underlying dimensions of attitude. Each dimension was then named and treated as anew variable, each measured by the factor scores. Both approach were applied separately to an analysis on compliance behavior of zakah on employment income. Results suggest that attitude measured by multidimensionality scores is more informative as compared to the single summative score. Futher, the use of multidimensional scores in multivariate logistic regression improved the goodness of fit of the model over that of the single score of attitude. Thus, this improvement affects the interpretation of the whole model with respect to the relationship between the independent variables and the dependent variable, which is zakah compliance.  

scholarly journals ANALISIS REGRESI KOMPONEN UTAMA UNTUK MENGATASI MASALAH MULTIKOLINIERITAS DALAM ANALISIS REGRESI LINIER BERGANDA

2012 ◽  
Vol 6 (1) ◽  
pp. 31-40
Author(s):  
Gresyea L. Marcus ◽  
Henry J. Wattimanela ◽  
Yopi A. Lesnussa
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Least Square Method ◽  
Component Analysis ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Analysis Technique ◽  
Independent Variable ◽  
Very High

The climate in Ambon, are influenced by sea climate and season climate, cause of this island arrounded by sea, it is make very high rainfall intensity. A very high collinearity between independent variables, make the estimate can not rely be ordinary least square method so it market with not real regretion coefficient and the collinearity. Collinearity can be detected by linier correlation coefficient between independent variables and also with VIF way. Regretion principal component analysis is used to remove collinearity and all of independent variable into model, this analysis is regretion analysis technique wher eare combinated with principal component analysis technique. The object of this analysis is to simplify the variable by overcast it dimension, we can do it removes the correlation between coefficient by transformation. Regresion can help to solve this case rainfall in Ambon on 2010. So the colinearity to independent variables can be overcome and then we can get the best regretion rutes.

scholarly journals Agro-morphological diversity of plantain accessions from different part of the world

2020 ◽  
Vol 14 (4) ◽  
pp. 1308-1321
Author(s):  
Amien Isaac Amoutchi ◽  
Oulo N’nan-Alla ◽  
Deless Edmond Fulgence Thiemele
Keyword(s):  
Principal Component Analysis ◽  
Genetic Improvement ◽  
Principal Component ◽  
Morphological Diversity ◽  
Black Sigatoka ◽  
Fruit Texture ◽  
Breeding Objective ◽  
The World ◽  
Qualitative Variables ◽  
Improvement Programme

The objective of this study was to characterize the agro-morphological diversity of plantain accessions. 18 quantitative variables and 20 qualitative variables were measured. The results of the analysis of the qualitative variables revealed important traits such as black Sigatoka resistance of FHIA 21, Pita 3, M53, Calculta 4 and Banskii accessions and the firm fruit texture of Galeo, Kokor, French sombre and Corne 1 accessions. A Principal Component Analysis (PCA) performed with the quantitative variables separated the 9 accessions into 4 groups with particular and important characteristics which can be exploited differently in genetic improvement programme according to the breeding objective. From these results, it appears clearly that the objective is achieved.Keywords: Sigatoka, qualitative variables, quantitative variables, genetic improvement.

scholarly journals Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier

2020 ◽  
Vol 13 (2) ◽  
pp. 11
Author(s):  
Bekti Endar Susilowati ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Large Area ◽  
Robust Principal Component Analysis ◽  
Data Set ◽  
Silhouette Width ◽  
The World

Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster. 

scholarly journals Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020 ◽  
Author(s):  
Edy Irwansyah ◽  
Ebiet Salim Pratama ◽  
Margaretha Ohyver
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Principal Component Analysis ◽  
Data Reduction ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Techniques ◽  
The World ◽  
Using Data

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

Sign in / Sign up

Export Citation Format

Share Document