Why do statisticians (econometricians, economists, financial analysts, etc.) continue to incompletely identify the algebraic/geometric structure of the multi-variate data series they profess to analyze, and instead continue to publish the results of incomplete, prejudiced and biased unidirectional projections (= 'regressions') of such covariance structures? Such incomplete, prejudiced and biased representations cannot lead to scientific knowledge, as has been demonstrated already more than twenty years ago.system identification, noisy data, regression analysis, projection, incompleteness, prejudice, bias
AbstractIn marketing and finance, surprisingly simple models sometimes predict more accurately than ...
There are over three decades of largely unrebutted criticism of regression analysis as practiced in ...
The article is aimed at reconsidering the question if the project of econometrics can be read in lin...
AbstractStatisticians have begun to realize that certain deliberately induced biases can dramaticall...
This study exposes the cognitive flaws of ‘endogeneity bias’. It examines how conceptualisation of t...
In this paper I discuss three issues related to bias of OLS estimators in a general multivariate set...
This companion paper to Chatelain and Ralf (2012), “Spurious regressions with near-multicollinearity...
This study exposes the flaw in defining endogeneity bias by correlation between an explanatory varia...
In medical research, frequently other important determinants, correlated with the key treatment vari...
Problems of the analysis of data with incomplete observations are all too familiar in statistics. Th...
Unobserved heterogeneity is common in economic data and has nontrivial impacts on modeling, estimati...
AbstractWe prove mathematically that the least squares regression scheme is of little use for identi...
Omitted variable bias (OVB) of OLS estimators is a serious and ubiquitous problem in social science ...
42 pagesThis article presents a particular case of spurious regression, when a dependent variable ha...
Soyer and Hogarth’s article, “The Illusion of Predictability,” shows that diagnostic statistics that...
AbstractIn marketing and finance, surprisingly simple models sometimes predict more accurately than ...
There are over three decades of largely unrebutted criticism of regression analysis as practiced in ...
The article is aimed at reconsidering the question if the project of econometrics can be read in lin...
AbstractStatisticians have begun to realize that certain deliberately induced biases can dramaticall...
This study exposes the cognitive flaws of ‘endogeneity bias’. It examines how conceptualisation of t...
In this paper I discuss three issues related to bias of OLS estimators in a general multivariate set...
This companion paper to Chatelain and Ralf (2012), “Spurious regressions with near-multicollinearity...
This study exposes the flaw in defining endogeneity bias by correlation between an explanatory varia...
In medical research, frequently other important determinants, correlated with the key treatment vari...
Problems of the analysis of data with incomplete observations are all too familiar in statistics. Th...
Unobserved heterogeneity is common in economic data and has nontrivial impacts on modeling, estimati...
AbstractWe prove mathematically that the least squares regression scheme is of little use for identi...
Omitted variable bias (OVB) of OLS estimators is a serious and ubiquitous problem in social science ...
42 pagesThis article presents a particular case of spurious regression, when a dependent variable ha...
Soyer and Hogarth’s article, “The Illusion of Predictability,” shows that diagnostic statistics that...
AbstractIn marketing and finance, surprisingly simple models sometimes predict more accurately than ...
There are over three decades of largely unrebutted criticism of regression analysis as practiced in ...
The article is aimed at reconsidering the question if the project of econometrics can be read in lin...