Search Results - subject:"PRINCIPAL COMPONENT ANALYSIS"

Hagströmer, Björn; Anderson, Richard G.; Binner, Jane M. - 2009

* Richard G. Anderson § Jane M. Binner ± Birger Nilsson May 26, 2009 We develop the principal component analysis (PCA … liquidity, market liquidity, commonality, dynamic principal component analysis, robust PCA JEL Codes: G10, C02 …. Anderson, Jane M. Binner, Birger Nilsson⁄ May 26, 2009 Abstract We develop the principal component analysis (PCA) approach to …

Persistent link: https://www.econbiz.de/10003844567

Growth regressions, principal components and frequentist model averaging

Wagner, Martin; Hlouskova, Jaroslava - 2009

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative...

Persistent link: https://www.econbiz.de/10009734674

Revisiting the Determinants of Productivity Growth : What's new?

Loko, Boileau - 2009

This paper studies the main determinants of total factor productivity (TFP) growth using principal component analysis …

Persistent link: https://www.econbiz.de/10014402271

Assessment of performance using MPSS based DEA

Sarkar, Subhadip - In: Benchmarking: An International Journal 22 (2015) 4, pp. 624-642

/methodology/approach – A non-central principal component analysis is used here to create a new plane according to the constant return to scale …

Persistent link: https://www.econbiz.de/10014683843

The influence of research and development policy: the case of Estonia in the EU

Reiljan, Janno; Paltser, Ingra - In: European Journal of Innovation Management 18 (2015) 3, pp. 307-329

indicators describing R & D policy measures was created for the business sector. Using principal component analysis (PCA) method …

Persistent link: https://www.econbiz.de/10014721761

Quantitative assessment of spiritual capital in changing organizations by principal component analysis and fuzzy clustering

Taghizadeh Yazdi, Mohammad Reza - In: Journal of Organizational Change Management 28 (2015) 3, pp. 469-485

) to assess SC in a two-phase integrated algorithm of principal component analysis (PCA) and fuzzy clustering. Findings …

Persistent link: https://www.econbiz.de/10014892185

Stock Selection with Principal Component Analysis

Yang, Libin; Rea, William; Rea, Alethea - Department of Economics and Finance, College of … - 2015

We propose a stock selection method that is based on a variable selection method used with principal component analysis …

Persistent link: https://www.econbiz.de/10011166615

An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making

Liu, Bingsheng; Shen, Yinghua; Zhang, Wei; Chen, Xiaohong; … - In: European Journal of Operational Research 245 (2015) 1, pp. 209-225

principal component analysis model and propose the interval-valued intuitionistic fuzzy principal component analysis model … transform these two types of variables into several independent variables using the proposed principal component analysis model …

Persistent link: https://www.econbiz.de/10011264342

Identification of different woody biomass for energy purpose by means of Soft Independent Modeling of Class Analogy applied to thermogravimetric analysis

Toscano, G.; Duca, D.; Rossini, G.; Mengarelli, C.; … - In: Energy 83 (2015) C, pp. 351-357

Principal Component Analysis and Soft Independent Modeling of Class Analogy as supervised pattern recognition method. …

Persistent link: https://www.econbiz.de/10011264373

Nested nonnegative cone analysis

Zhang, Lingsong; Lu, Shu; Marron, J.S. - In: Computational Statistics & Data Analysis 88 (2015) C, pp. 100-110

Motivated by the analysis of nonnegative data objects, a novel Nested Nonnegative Cone Analysis (NNCA) approach is proposed to overcome some drawbacks of existing methods. The application of traditional PCA/SVD method to nonnegative data often cause the approximation matrix leave the nonnegative...

Persistent link: https://www.econbiz.de/10011264466