Deterministic parallel analysis: an improved method for selecting factors and principal components
نویسندگان
چکیده
منابع مشابه
Parallel Analysis: a Method for Determining Significant Principal Components
Numerous ecological studies use Principal Components Analysis (PCA) for exploratory analysis and data reduction. Determination of the number of components to retain is the most crucial problem confronting the researcher when using PCA. An incorrect choice may lead to the underextraction of components, but commonly results in overextraction. Of several methods proposed to determine the significa...
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Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
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Given an input array of n real numbers, sorting with padding consists in writing those numbers in order in an array of size (1 +)n, thus leaving n entries empty. Only comparisons are allowed between the numbers to be sorted. We describe an algorithm that, with nk processors in the CRCW PRAM model, sorts the input array with padding o(1) using time O(log k n log (log k n) + loglog k). This impro...
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متن کامل[Method of simulation and choice of factors in the analysis of principal components].
OBJECTIVE There are many methods to determine how many components should be retained in principal components analysis. This choice can be made on the basis of arbitrary (Kaiser) or subjective (Interpretable factors) criteria. This work presents the simulation criteria of Lébart e Dreyfus. The method create a matrix of randomized numbers and a principal component analysis is performed on the bas...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2018
ISSN: 1369-7412
DOI: 10.1111/rssb.12301