scholarly journals Measuring local competitiveness: comparing and integrating two methods PCA and AHP

2021 ◽  
Author(s):  
Katarzyna A. Kurek ◽  
Wim Heijman ◽  
Johan van Ophem ◽  
Stanisław Gędek ◽  
Jacek Strojny
Keyword(s):  
Social Sciences ◽  
Principal Component Analysis ◽  
Analytical Hierarchy Process ◽  
Mixed Method ◽  
Principal Component ◽  
Component Analysis ◽  
Mixed Modelling ◽  
Hierarchy Process ◽  
Comparable Model ◽  
Non Parametric

AbstractThis article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination.

Principal component analysis based on non-parametric maximum entropy

2010 ◽  
Vol 73 (10-12) ◽  
pp. 1840-1852 ◽  
Author(s):  
Ran He ◽  
Baogang Hu ◽  
XiaoTong Yuan ◽  
Wei-Shi Zheng
Keyword(s):  
Principal Component Analysis ◽  
Maximum Entropy ◽  
Principal Component ◽  
Component Analysis ◽  
Non Parametric

scholarly journals An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

2020 ◽  
Vol 12 (3) ◽  
pp. 789 ◽  
Author(s):  
Ramin Gharizadeh Beiragh ◽  
Reza Alizadeh ◽  
Saeid Shafiei Kaleibari ◽  
Fausto Cavallaro ◽  
Sarfaraz Zolfani ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Performance Evaluation ◽  
Performance Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Business Environment ◽  
Insurance Companies ◽  
Bottom Line ◽  
Sustainability Performance ◽  
Hierarchy Process

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

scholarly journals ALGORITMA PRINCIPAL COMPONEN ANALYSIS DALAM PEMROSESAN SINYAL ELECTROKARDIOGRAM

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Husaini Husaini ◽  
Huzaeni Huzaeni ◽  
Fahmi Fahmi
Keyword(s):  
Principal Component Analysis ◽  
Parametric Method ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Redundant Data ◽  
The Cost ◽  
Pca Algorithm ◽  
Non Parametric

Abstrak — Principal Component Analysis (PCA) merupakan salah satu teknik yang ada dalam statistic dan merupakan metode non parametric untuk mengekstraksi informasi-informasi yang bersesuaian dari sekumpulan data yang masih diragukan dan memerlukan proses untuk menghilangkan gangguan-gangguan yang ada. Data yang dimaksud salah satunya adalah sinyal ektrokardiogram (EKG). Sinyal EKG merupakan sinyal yang diperoleh dari rekaman aktifitas elektrik dari jantung. Rekaman sinyal EKG tidak saja digunakan untuk tujuan diagnosa, tapi juga disimpan sebagai referensi dalam mengklasifikasi EKG arrhythmia. Untuk mendapatkan hasil yang lebih baik maka data-data sinyal EKG akan direduksi dimensinya dengan tujuan untuk menghilangkab data-data yang tidak sesuai, tidak relevan dan data redundant sehingga dapat menghemat biaya komputasinya dan mencegah data-data yang over-fitting. Tulisan ini memaparkan tentang ide dasar dari PCA dalam mereduksi dimensi data-data dari sinyal  EKG. Hasil yang ditampilkan adalah berupa proses-proses dalam algoritma PCA dan akurasi klasifikasi sinyal  dengan metode KNN dan Naive Bayes.Kata kunci : principal component analysisi (PCA), sinyal EKG, reduksi dimensi Abstract — The Principal Component Analysis (PCA) is one of the existing techniques in statistics and a non parametric method for extracting the information from a collection of data that still in doubt and requires a process to remove any disturbances. The data in question one of them is the signal ektrokardiogram (ECG). ECG signals are signals obtained from recording electrical activity from the heart. ECG signal recording is not only used for diagnostic purposes, but is also stored as a reference in classifying ECG arrhythmias. To get better results then the ECG signal data will be reduced the dimension. The aim to removed data that are not appropriate, irrelevant and redundant data so as to save the cost of computing and prevent data over-fitting. This paper describes the basic idea of PCA in reducing the dimensions of data from ECG signals. The results shown are the processes in PCA algorithm and signal classification accuracy by KNN and Naive Bayes methods.Keywords— Principal Component Analysis, ECG Signal, reduction dimentionality

Composition of Cellular Subsets by Flow Cytometry Identifies Differences Between MDS Subtypes and Aplastic Anemia but No Differences Are Identified Between Cases with and without Monosomy 7.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3802-3802
Author(s):  
Ester Mejstrikova ◽  
Vendula Pelkova ◽  
Michaela Reiterová ◽  
Martina Sukova ◽  
Zuzana Zemanova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Flow Cytometry ◽  
B Cells ◽  
B Cell ◽  
Aplastic Anemia ◽  
Principal Component ◽  
Component Analysis ◽  
Monosomy 7 ◽  
Parametric Statistics ◽  
Non Parametric

Abstract Abstract 3802 Poster Board III-738 Introduction Monosomy 7 or del(7q) are frequent cytogenetic abnormalities in children with myelodysplastic syndrome (MDS) and associates with poor prognosis. MDS globally affects all cellular subsets in bone marrow and in peripheral blood. We asked whether flow cytometry (FC) can separate individual subtypes of MDS from each other and from aplastic anemia (SAA) and whether in individual subtypes of childhood MDS can separate patients with and without monosomy 7. Patients/analyzed parameters In total we analyzed 94 children with centrally analyzed immunophenotype in the reference lab who were diagnosed and treated for MDS or SAA between 1998 and 2009. In total we analyzed 14 patients with refractory cytopenia, 37 patients with advanced forms of MDS (JMML 10, RAEB 25, CMML 2) and 43 patients with SAA. Monosomy 7/del(7q) was present in 17 patients (RC 6, JMML 3, RAEB 8). Analyzed parameters were as follows: B cells, CD10+CD19+, CD19+45dim/neg, CD19+34+, CD19/CD34 ratio, CD34+, CD117 cells, CD34+38dim/neg, CD3+, CD3+4+, CD3+8+, CD3+HLADR+. Statistics We analyzed all parameters using non parametric tests (Mann-Whitney, Kruskal Wallis) and principal component analysis (PCA). Results Principal component analysis of all analyzed patients together clearly separates advanced forms of MDS from RC and SAA, the most contributing factor being the number of CD34 and CD117+ cells. In non parametric statistics following factors significantly differ among MDS subtypes and SAA (Kruskal-Wallis): CD19, CD117, CD34, CD3, CD3+4+, CD8+ and CD3+HLADR+. RC and SAA patients are separated mainly by the number of B cells and the CD34:CD19 ratio. In addition, the following parameters differ between RC and SAA (Mann-Whitney): CD34, CD117 and CD3+HLADR+. Unlike the CD34:CD19 ratio, the number of CD19+34+ precursors does not differ between RC and SAA patients. Patients with monosomy 7 do not differ from the remaining patients when all MDS patients are analyzed together or separately in the respective subgroups (RC, non RC, JMML) by PCA or by non parametric statistics. Conclusion PCA separates advanced MDS forms from RC and SAA. Advanced forms of MDS are characterized by increased percentage of CD34+ and CD117+ cells compared to RC and SAA patients. The global reduction of B cell progenitor compartment is pronounced especially in non-JMML cases of MDS, whereas SAA patients typically present with isolated reduction of cells at early stages (CD19+34+) of B cell development. Patients with monosomy 7 cluster within the respective disease category, they do not form own cluster in PCA. Supported by MSMT VZ MSM0021620813, MZO 00064203 VZ FNM, MZO VFN2005, IGA NR/9531-3, NPV 2B06064. Disclosures: No relevant conflicts of interest to declare.

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