| dc.creator |
Evans, George W. |
|
| dc.creator |
Honkapohja, Seppo |
|
| dc.creator |
Williams, Noah |
|
| dc.date |
2005 |
|
| dc.date.accessioned |
2013-10-16T07:02:31Z |
|
| dc.date.available |
2013-10-16T07:02:31Z |
|
| dc.date.issued |
2013-10-16 |
|
| dc.identifier |
http://hdl.handle.net/10419/19040 |
|
| dc.identifier |
ppn:503712469 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/10419/19040 |
|
| dc.description |
We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity. |
|
| dc.language |
eng |
|
| dc.publisher |
|
|
| dc.relation |
CESifo working papers 1576 |
|
| dc.rights |
http://www.econstor.eu/dspace/Nutzungsbedingungen |
|
| dc.subject |
C65 |
|
| dc.subject |
C62 |
|
| dc.subject |
E17 |
|
| dc.subject |
E10 |
|
| dc.subject |
D83 |
|
| dc.subject |
ddc:330 |
|
| dc.subject |
adaptive learning |
|
| dc.subject |
E-stability |
|
| dc.subject |
recursive least squares |
|
| dc.subject |
robust estimation |
|
| dc.subject |
Rationale Erwartung |
|
| dc.subject |
Lernprozess |
|
| dc.subject |
Prognoseverfahren |
|
| dc.subject |
Gleichgewichtsstabilität |
|
| dc.subject |
Theorie |
|
| dc.title |
Generalized stochastic gradient learning |
|
| dc.type |
doc-type:workingPaper |
|