ON VARIABLE SELECTION IN LINEAR REGRESSION
Shibata (1981, Biometrika 68, 45–54) considers data-generating mechanisms belonging to a certain class of linear regressions with errors that are independent and identically normally distributed. He compares the variable selection criteria AIC (Akaike information criterion) and BIC (Bayesian informa...
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| Veröffentlicht in: | Econometric theory 2002-08, Vol.18 (4), p.913-925 |
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| creator | Kabaila, Paul |
| description | Shibata (1981, Biometrika 68, 45–54) considers
data-generating mechanisms belonging to a certain class of linear
regressions with errors that are independent and identically
normally distributed. He compares the variable selection criteria
AIC (Akaike information criterion) and BIC (Bayesian information
criterion) using the following type of comparison. For each
fixed possible data–generating mechanism, these criteria
are compared as the data length increases. The results of this
comparison have been interpreted as meaning that, in the context
of the data-generating mechanisms considered by Shibata, AIC
is better than BIC for large data lengths. Shibata's
comparison is pointwise in the space of data–generating
mechanisms (as the data length increases). Such comparisons
are potentially misleading. We consider a simple class of
data-generating mechanisms satisfying Shibata's assumptions
and carry out a different type of comparison. For each fixed
data length (possibly large) we compare the variable selection
criteria for every possible data-generating mechanism in this
class. According to this comparison, for this class of
data-generating mechanisms no matter how large the data length
AIC is not better than BIC. |
| doi_str_mv | 10.1017/S0266466602184052 |
| format | Article |
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data-generating mechanisms belonging to a certain class of linear
regressions with errors that are independent and identically
normally distributed. He compares the variable selection criteria
AIC (Akaike information criterion) and BIC (Bayesian information
criterion) using the following type of comparison. For each
fixed possible data–generating mechanism, these criteria
are compared as the data length increases. The results of this
comparison have been interpreted as meaning that, in the context
of the data-generating mechanisms considered by Shibata, AIC
is better than BIC for large data lengths. Shibata's
comparison is pointwise in the space of data–generating
mechanisms (as the data length increases). Such comparisons
are potentially misleading. We consider a simple class of
data-generating mechanisms satisfying Shibata's assumptions
and carry out a different type of comparison. For each fixed
data length (possibly large) we compare the variable selection
criteria for every possible data-generating mechanism in this
class. According to this comparison, for this class of
data-generating mechanisms no matter how large the data length
AIC is not better than BIC.</description><identifier>ISSN: 0266-4666</identifier><identifier>EISSN: 1469-4360</identifier><identifier>DOI: 10.1017/S0266466602184052</identifier><language>eng</language><publisher>New York, USA: Cambridge University Press</publisher><subject>Econometrics ; Economic models ; Efficiency metrics ; Error ; Estimation ; Estimators ; Integers ; Least squares method ; Linear models ; Linear regression ; Mathematical vectors ; Regression analysis ; Space mechanics ; Statistical methods ; Statistics</subject><ispartof>Econometric theory, 2002-08, Vol.18 (4), p.913-925</ispartof><rights>2002 Cambridge University Press</rights><rights>Copyright 2002 Cambridge University Press</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-2364086e5c778cee3f8c12b75cc0286c2d97a23d5a0f60c600ea00082a4dcdea3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3533418$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0266466602184052/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,799,27901,27902,55603,57992,58225</link.rule.ids></links><search><creatorcontrib>Kabaila, Paul</creatorcontrib><title>ON VARIABLE SELECTION IN LINEAR REGRESSION</title><title>Econometric theory</title><addtitle>Econom. Theory</addtitle><description>Shibata (1981, Biometrika 68, 45–54) considers
data-generating mechanisms belonging to a certain class of linear
regressions with errors that are independent and identically
normally distributed. He compares the variable selection criteria
AIC (Akaike information criterion) and BIC (Bayesian information
criterion) using the following type of comparison. For each
fixed possible data–generating mechanism, these criteria
are compared as the data length increases. The results of this
comparison have been interpreted as meaning that, in the context
of the data-generating mechanisms considered by Shibata, AIC
is better than BIC for large data lengths. Shibata's
comparison is pointwise in the space of data–generating
mechanisms (as the data length increases). Such comparisons
are potentially misleading. We consider a simple class of
data-generating mechanisms satisfying Shibata's assumptions
and carry out a different type of comparison. For each fixed
data length (possibly large) we compare the variable selection
criteria for every possible data-generating mechanism in this
class. According to this comparison, for this class of
data-generating mechanisms no matter how large the data length
AIC is not better than BIC.</description><subject>Econometrics</subject><subject>Economic models</subject><subject>Efficiency metrics</subject><subject>Error</subject><subject>Estimation</subject><subject>Estimators</subject><subject>Integers</subject><subject>Least squares method</subject><subject>Linear models</subject><subject>Linear regression</subject><subject>Mathematical vectors</subject><subject>Regression analysis</subject><subject>Space mechanics</subject><subject>Statistical 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theory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kabaila, Paul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ON VARIABLE SELECTION IN LINEAR REGRESSION</atitle><jtitle>Econometric theory</jtitle><addtitle>Econom. 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data-generating mechanisms belonging to a certain class of linear
regressions with errors that are independent and identically
normally distributed. He compares the variable selection criteria
AIC (Akaike information criterion) and BIC (Bayesian information
criterion) using the following type of comparison. For each
fixed possible data–generating mechanism, these criteria
are compared as the data length increases. The results of this
comparison have been interpreted as meaning that, in the context
of the data-generating mechanisms considered by Shibata, AIC
is better than BIC for large data lengths. Shibata's
comparison is pointwise in the space of data–generating
mechanisms (as the data length increases). Such comparisons
are potentially misleading. We consider a simple class of
data-generating mechanisms satisfying Shibata's assumptions
and carry out a different type of comparison. For each fixed
data length (possibly large) we compare the variable selection
criteria for every possible data-generating mechanism in this
class. According to this comparison, for this class of
data-generating mechanisms no matter how large the data length
AIC is not better than BIC.</abstract><cop>New York, USA</cop><pub>Cambridge University Press</pub><doi>10.1017/S0266466602184052</doi><tpages>13</tpages></addata></record> |
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| ispartof | Econometric theory, 2002-08, Vol.18 (4), p.913-925 |
| issn | 0266-4666 1469-4360 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_39092741 |
| source | Jstor Complete Legacy; Cambridge Journals |
| subjects | Econometrics Economic models Efficiency metrics Error Estimation Estimators Integers Least squares method Linear models Linear regression Mathematical vectors Regression analysis Space mechanics Statistical methods Statistics |
| title | ON VARIABLE SELECTION IN LINEAR REGRESSION |
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