Statistical Factor Analysis and Related Methods: Theory and Applications

By J. Jackson, A. Basilevsky. 1995

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Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “Gaussian CCA is subject to some well-known unidentifiability issues, in the same way as the closely related factor analysis model (FA; Bartholomew, 1987; Basilevsky, 1994) and its special case, the probabilistic principal component analysis model (PPCA; Tipping & Bishop, 1999; Roweis, 1998).”
Factor analysis applied to regional geochemical data: problems and possibilities (2002, methodology): “For a correct statistical investigation, the data should be viewed as a realisation of a stochastic process, which will result in complicated and expensive statistical procedures (see, e.g. Basilevsky, 1994).”
Physiographical space‐based kriging for regional flood frequency estimation at ungauged sites (2004, methodology): “[12] Principal component analysis (PCA) is an exploratory multivariate statistical method for simplifying complex data sets [Jackson, 1991; Basilevsky, 1994].”
The Hull Method for Selecting the Number of Common Factors (2011, background): “…freedom in an EFA of a correlation matrix that involves m variables and q factors is the number of unique variances plus the number of entries in the pattern matrix minus the number of entries that are set to zero by an orthogonal transformation of the factors (see, e.g., Basilevsky, 1994, p. 356).”
The benefits of being present: mindfulness and its role in psychological well-being. (2003, background): “Items 5 and 13 loaded above .25 and were retained because both added substantive breadth to the scale (cf. Basilevsky, 1994; Tabachnick & Fidell, 1996).”
Innovation and Economic Development (2009, methodology): “An alternative approach, pioneered by Adelman and Morris (1965, 1967), uses so-called “factor analysis” (Basilevsky 1994) to advise on questions like these.”
National innovation systems, capabilities and economic development (2008, methodology): “An alternative approach, pioneered by Adelman and Morris (1965, 1967), uses so-called “factor analysis” (Basilevsky, 1994) to advice on questions like this.”
Dictionary Learning Algorithms for Sparse Representation (2003, methodology): “It is well known (Basilevsky, 1994) that the stochastic generative model y = Ax + ν, (1.3) can be used to develop algorithms enabling coding of y ∈ R m via solving the inverse problem for a sparse solution x ∈ R n for the undercomplete (n m) and complete (n = m) cases.”
Pattern Recognition and Machine Learning (2006, unknown): “As we shall see later, it is closely related to factor analysis (Basilevsky, 1994).”
Estimating common trends in multivariate time series using dynamic factor analysis (2003, background): “For example, a varimax rotation (Basilevsky, 1994) will attempt to relate each time series to just one of the common trends.”