Revisiting data mining: 'hunting' with or without a license
The primary objective of this paper is to revisit a number of empirical modelling activities which are often characterized as data mining, in an attempt to distinguish between the problematic and the non-problematic cases. The key for this distinction is provided by the notion of error-statistical severity. It is argued that many unwarranted data mining activities often arise because of inherent weaknesses in the Traditional Textbook (TT) methodology. Using the Probabilistic Reduction (PR) approach to empirical modelling, it is argued that the unwarranted cases of data mining can often be avoided by dealing directly with the weaknesses of the TT approach. Moreover, certain empirical modelling activities, such as diagnostic testing and data snooping, constitute legitimate procedures in the context of the PR approach.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 7 (2001)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RJEC20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RJEC20|
When requesting a correction, please mention this item's handle: RePEc:taf:jecmet:v:7:y:2001:i:2:p:231-264. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.