scholarly journals Beyond trees: common-factor models for 2D human pose recovery

2005 ◽  
Author(s):  
Xiangyang Lan ◽  
D.P. Huttenlocher
Keyword(s):  
Common Factor ◽  
Factor Models ◽  
Pose Recovery ◽  
Human Pose ◽  
Common Factor Models

On the Mining of Cointegrated Econometric Models

2010 ◽  
pp. 122-135
Author(s):  
J.L. van Velsen ◽  
R. Choenni
Keyword(s):  
Common Factor ◽  
Special Kind ◽  
Information Criterion ◽  
Factor Models ◽  
Econometric Models ◽  
Heuristic Model ◽  
Future Developments ◽  
Model Generator ◽  
Common Factor Models

The authors describe a process of extracting a cointegrated model from a database. An important part of the process is a model generator that automatically searches for cointegrated models and orders them according to an information criterion. They build and test a non-heuristic model generator that mines for common factor models, a special kind of cointegrated models. An outlook on potential future developments is given.

Alternative common factor models for multivariate biometric analyses

1990 ◽  
Vol 20 (5) ◽  
pp. 569-608 ◽  
Author(s):  
J. J. McArdle ◽  
H. H. Goldsmith
Keyword(s):  
Common Factor ◽  
Factor Models ◽  
Common Factor Models

Price discovery and common factor models

2002 ◽  
Vol 5 (3) ◽  
pp. 309-321 ◽  
Author(s):  
Richard T. Baillie ◽  
G. Geoffrey Booth ◽  
Yiuman Tse ◽  
Tatyana Zabotina
Keyword(s):  
Price Discovery ◽  
Common Factor ◽  
Factor Models ◽  
Common Factor Models

Three common factor models for groups of variables

Psychometrika ◽  
1970 ◽  
Vol 35 (1) ◽  
pp. 111-128 ◽  
Author(s):  
Roderick P. McDonald
Keyword(s):  
Common Factor ◽  
Factor Models ◽  
Common Factor Models

scholarly journals WorseThanMeasurementError

2018 ◽  
Author(s):  
Mijke Rhemtulla ◽  
Riet van Bork ◽  
Denny Borsboom
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Factor Models ◽  
Model Fit ◽  
Structural Parameter ◽  
Parameter Estimates ◽  
Psychological Variables ◽  
Common Factor Model ◽  
Composite Models ◽  
Common Factor Models

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not generated from a common factor construct model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical re-analysis, we show that (1) common factor models tend to produce extremely biased and highly variable structural parameter estimates when the population model is not a common factor model; (2) model fit is a poor indicator of the degree of bias; and (3) composite models are sometimes more reliable than common factor models under alternative measurement structures, though they also lead to unacceptably bad solutions in some cases.

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