Factor regression model
Appearance
Within statistical factor analysis, the factor regression model,[1] or hybrid factor model,[2] is a special multivariate model with the following form:
where,
- is the -th (known) observation.
- is the -th sample (unknown) hidden factors.
- is the (unknown) loading matrix of the hidden factors.
- is the -th sample (known) design factors.
- is the (unknown) regression coefficients of the design factors.
- is a vector of (unknown) constant term or intercept.
- is a vector of (unknown) errors, often white Gaussian noise.
Relationship between factor regression model, factor model and regression model
[edit]The factor regression model can be viewed as a combination of factor analysis model () and regression model ().
Alternatively, the model can be viewed as a special kind of factor model, the hybrid factor model [2]
where, is the loading matrix of the hybrid factor model and are the factors, including the known factors and unknown factors.
Software
[edit]Open source software to perform factor regression is available.
References
[edit]- ^ Carvalho, Carlos M. (1 December 2008). "High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics". Journal of the American Statistical Association. 103 (484): 1438–1456. doi:10.1198/016214508000000869. PMC 3017385. PMID 21218139.
- ^ a b Meng, J. (2011). "Uncover cooperative gene regulations by microRNAs and transcription factors in glioblastoma using a nonnegative hybrid factor model". International Conference on Acoustics, Speech and Signal Processing. Archived from the original on 2011-11-23.