Permutimin: Factor Rotation to Simple Structure with Permutation of Variables

Yamashita, Naoto; Adachi, Keiichiro

doi:10.1080/00273171.2019.1598331

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Principal component method (PCA) is an appropriate multivariate method that helps to reduce the dimensionality of a data set consisting of a large number of interrelated variables while maintaining as much variation as possible. The practical foundations of the methodology for determining the number of Factors are presented in [12][13][14], the rotation methods of Factor analysis such as orthogonal and oblique are presented in [15][16][17][18], and the use of Factor estimates in multiple regression analysis is in [19].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Factors of pricing of agricultural products

Tetiana¹

2023

Herald of KNUTE

View full text Add to dashboard Cite

Introduction. Price is the main factor that has a direct impact on the main results of the enterprises, especially agricultural. The pricing policy is able to maintain the competitiveness of the enterprise, to ensure its solvency and stability. Problem. Pricing is carried out in a complex interaction of a set of internal and external Factors. The corresponding complexity of the phenomenon leads to the need to include many Factors in the model, which can negatively affect the result. The numerous number of Factors necessitate the use of methods that allow the identification of the most statistically significant Factors and assess their relationship with the resulting feature. The aim of the article is to identify the main Factors and their influence on the formation and change of the price of agricultural products using Factor analysis. Methods. Different methods such as mathematical and statistical (factor analysis, principal component), economic and statistical (multiple regression method based on factor scores) and generalization were applied in the article. Results. The application of Factor analysis and method of principal components determine the most significant Factors influencing the formation of agricultural products prices. The author used different dimensionality reduction methods and obtained three main components: supply, demand, and the solvency of the population. This helped to reveal the relationship between variables and price without multicollinearity problem. Conclusions. The author used Factor analysis and the principal component method; it helped to take into account the peculiarities of the relationships between the indicators that characterize potato pricing, to eliminate multicollinearity between independent indicators, to get fewer common Factors than the original number of variables, to obtain a quantitative assessment of the latent variable. The use of orthogonal rotation helped to distribute the load on all three Factors more evenly, which simplified their further interpretation and to save 95 % of the total primary information after rotation and to reduce the dimensionality of the feature space, which is a high indicator for practical tasks.

show abstract

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Factors of pricing of agricultural products

Tetiana¹

2023

Herald of KNUTE

View full text Add to dashboard Cite

show abstract

“…Adachi [13] proposed a procedure for estimating the target matrix by modifying Thurstone's simple structure by improving Simplimax.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Procrustes Rotation with Sparse Target Matrix and Least Squares Criterion with Regularization and Generalized Weighting

Yamashita¹

2023

OJS

Self Cite

View full text Add to dashboard Cite

In factor analysis, a factor loading matrix is often rotated to a simple target matrix for its simplicity. For the purpose, Procrustes rotation minimizes the discrepancy between the target and rotated loadings using two types of approximation: 1) approximate the zeros in the target by the non-zeros in the loadings, and 2) approximate the non-zeros in the target by the non-zeros in the loadings. The central issue of Procrustes rotation considered in the article is that it equally treats the two types of approximation, while the former is more important for simplifying the loading matrix. Furthermore, a well-known issue of Simplimax is the computational inefficiency in estimating the sparse target matrix, which yields a considerable number of local minima. The research proposes a new rotation procedure that consists of the following two stages. The first stage estimates sparse target matrix with lesser computational cost by regularization technique. In the second stage, a loading matrix is rotated to the target, emphasizing on the approximation of non-zeros to zeros in the target by least squares criterion with generalized weighing that is newly proposed by the study. The simulation study and real data examples revealed that the proposed method surely simplifies loading matrices.

show abstract

Exploratory extended redundancy analysis using sparse estimation and oblique rotation of parameter matrices

Yamashita

2023

Behaviormetrika

View full text Add to dashboard Cite

Permutimin: Factor Rotation to Simple Structure with Permutation of Variables

Cited by 3 publications

References 19 publications

Factors of pricing of agricultural products

Factors of pricing of agricultural products

Two-Stage Procrustes Rotation with Sparse Target Matrix and Least Squares Criterion with Regularization and Generalized Weighting

Exploratory extended redundancy analysis using sparse estimation and oblique rotation of parameter matrices

Contact Info

Product

Resources

About