Pages that link to "Item:Q2012652"

The following pages link to A rationale and test for the number of factors in factor analysis (Q2012652):

Displaying 48 items.

• Bi-cross-validation for factor analysis (Q104117) (← links)
• On the construction of all factors of the model for factor analysis (Q463092) (← links)
• Determining the number of factors when the number of factors can increase with sample size (Q506051) (← links)
• Considering Horn's parallel analysis from a random matrix theory point of view (Q525237) (← links)
• An exact approach to sparse principal component analysis (Q638039) (← links)
• Inferring constructs of effective teaching from classroom observations: an application of Bayesian exploratory factor analysis without restrictions (Q902925) (← links)
• How many principal components? Stopping rules for determining the number of non-trivial axes revisited (Q957273) (← links)
• Commentary on coefficient alpha: a cautionary tale (Q1013060) (← links)
• A decision procedure for determining the number of components in principal component analysis (Q1193973) (← links)
• A proposal for handling missing data (Q1219530) (← links)
• Manifold-based synthetic oversampling with manifold conformance estimation (Q1640402) (← links)
• The rank of reduced dispersion matrices (Q1820531) (← links)
• Robust high-dimensional factor models with applications to statistical machine learning (Q2038305) (← links)
• Estimating change-point latent factor models for high-dimensional time series (Q2059427) (← links)
• A note on the likelihood ratio test in high-dimensional exploratory factor analysis (Q2066588) (← links)
• Testing for the rank of a covariance operator (Q2112827) (← links)
• A systematic study into the factors that affect the predictive accuracy of multilevel VAR(1) models (Q2152398) (← links)
• Permutation methods for factor analysis and PCA (Q2215761) (← links)
• Rapid evaluation of the spectral signal detection threshold and Stieltjes transform (Q2230693) (← links)
• Dandelion plot: a method for the visualization of R-mode exploratory factor analyses (Q2259825) (← links)
• On a test of dimensionality in redundancy analysis (Q2260053) (← links)
• Defining probability density for a distribution of random functions (Q2380100) (← links)
• Hypothesis tests for principal component analysis when variables are standardized (Q2419845) (← links)
• A second generation little Jiffy (Q2544712) (← links)
• Model-based clustering (Q2628064) (← links)
• Common time variation of parameters in reduced-form macroeconomic models (Q2691652) (← links)
• Factor Analysis Revisited – How Many Factors are There? (Q3072420) (← links)
• Deterministic Parallel Analysis: An Improved Method for Selecting Factors and Principal Components (Q3120105) (← links)
• A Comparison of Methods for Approximating the Mean Eigenvalues of a Random Matrix (Q3155661) (← links)
• Assessing the Finite Dimensionality of Functional Data (Q3408555) (← links)
• A Simple Rule for the Selection of Principal Components (Q4798095) (← links)
• Econometric Mediation Analyses: Identifying the Sources of Treatment Effects from Experimentally Estimated Production Technologies with Unmeasured and Mismeasured Inputs (Q5080522) (← links)
• Minimum average partial correlation and parallel analysis: The influence of oblique structures (Q5087486) (← links)
• Parallel analysis approach for determining dimensionality in canonical correlation analysis (Q5221541) (← links)
• An estimation of the number of primary factors based on residual matrix variance ratio (Q5260424) (← links)
• A Note on Unwanted Variance in Exploratory Factor Models (Q5299095) (← links)
• The Matrices of Factor Analysis (Q5841625) (← links)
• Discrimination with unidimensional and multidimensional item response theory models for educational data (Q5866138) (← links)
• The independent component analysis with the linear regression – predicting the energy costs of the public sector buildings in Croatia (Q5872970) (← links)
• Factor analysis of correlation matrices when the number of random variables exceeds the sample size (Q5880184) (← links)
• Learning Gaussian graphical models with latent confounders (Q6051077) (← links)
• Estimation of the Number of Spiked Eigenvalues in a Covariance Matrix by Bulk Eigenvalue Matching Analysis (Q6107215) (← links)
• Dyadic analysis for multi-block data in sport surveys analytics (Q6170913) (← links)
• Efficient change point detection and estimation in high-dimensional correlation matrices (Q6200899) (← links)
• Regularized variational estimation for exploratory item factor analysis (Q6572344) (← links)
• An heuristic scree plot criterion for the number of factors (Q6581367) (← links)
• How much do knowledge about and attitude toward mobile phone use affect behavior while driving? An empirical study using a structural equation model (Q6614830) (← links)
• A principal-weighted penalized regression model and its application in economic modeling (Q6662620) (← links)