2 edition of **Information criteria for discriminating among alternative regression models** found in the catalog.

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- 40 Currently reading

Published
**1978**
by College of Commerce and Business Administration, University of Illinois at Urbana-Champaign in [Urbana]
.

Written in English

- Distribution (Probability theory),
- Information theory

**Edition Notes**

Bibliography: leaf 33.

Statement | Takamitsu Sawa |

Series | Faculty working papers -- no. 455, Faculty working papers -- no. 455. |

The Physical Object | |
---|---|

Pagination | 33 leaves ; |

Number of Pages | 33 |

ID Numbers | |

Open Library | OL24980422M |

OCLC/WorldCa | 5086038 |

Model-Selection Methods Model Selection Issues Criteria Used in Model Selection Methods CLASS Variable Parameterization and the SPLIT Option Macro Variables Containing Selected Models Using the STORE Statement Collier Books (), The (), "Information Criteria for Discriminating among Alternative Regression Models. Sawa, T. (): Information Criteria for Discriminating Among Alternative Regression Models. Econometrica, 46(6): – Sawa T., ' Information Criteria for Discriminating Among Alternative Regression Models ' () 46 Econometrica:

LaMotte, L.R. (), "A Note on the Role of Independence in t Statistics Constructed From Linear Statistics in Regression Models," The American Statistician, 48, Lord, F.M. (), "Efficiency of Prediction when a Progression Equation from One Sample is Used in a New Sample," Research Bulletin No. , Princeton, NJ: Educational. Extended Fisher Information Criterion (EFIC) is a model selection criterion for linear regression models. Among these criteria, cross-validation is typically the most accurate, and computationally the most expensive, for supervised learning problems. Burnham & Anderson (, §) say the following (with wikilinks added).

Model-Selection Methods; Criteria Used in Model-Selection Methods; Limitations in Model-Selection Methods; “Information Criteria for Discriminating among Alternative Regression Models.” Econometrica – Schwarz, G. (). “Estimating the Dimension of a Model.” Annals of Statistics – Package ‘gtWAS’ June 1, Type Package Title Genome and Transcriptome Wide Association Study Version Date Author JunhuiLi WenxinLiu.

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Theaboveconsiderationleadsusnaturallytotheso-calledprin- cipleofparsimony Thatis,moreparsimonioususeofparametersshould. Sawa, Takamitsu, "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol.

46(6), pages Cited by: Sawa, T.,Information criteria for discriminating among alternative regression models, Econometr Stone, M.,Comments on model selection criteria of Akaike and Schwarz, Journal of the Royal Statistical Society B41, Cited by: Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): (external link)Author: Takamitsu Sawa.

By Takamitsu Sawa; Information Criteria for Discriminating among Alternative Regression ModelsCited by: Information criteria for discriminating among alternative regression models / By Takamitsu.

Sawa. Abstract. Bibliography: leaf Mode of access: Internet Topics: Information theory., Distribution (Probability theory) Publisher: [Urbana]: College. The literature on information criteria is vast; see, among others,Akaike(), Sawa(), andRaftery(). Judge et al. () contains a discussion of using information criteria in n and Sauerbrei(, chap.

2) examine the use of information criteria as an alternative to stepwise procedures for selecting model variables. Stepwise regression analysis can be performed with univariate and multivariate based on information criteria specified, which includes 'forward', 'backward' and 'bidirection' direction model selection method.

Also continuous variables nested within class effect and weighted stepwise are considered. Information criteria for discriminating among alternative regression models / BEBR No. By Takamitsu Sawa Download PDF (2 MB). Statistics & Probability Letters 11 () North-Holland An information criterion for normal regression estimation Ehsan S.

Soofi School of Business Administration, University of Wisconsin-Milwaukee, P. BoxMilwaukee, WIUSA D. Gokhale Department of Statistics, Uniuersity of California, Riverside, CAUSA Received April Revised March.

This paper discusses the topic of model selection for finite-dimensional normal regression models. We compare model selection criteria according to prediction errors based upon prediction with refitting, and prediction without refitting. Sawa, T.

(), “Information criteria for discriminating among alternative regression models”. Econometr – MathSciNet zbMATH CrossRef Google Scholar. Definition. Suppose that we have a statistical model of some data.

Let k be the number of estimated parameters in the model. Let ^ be the maximum value of the likelihood function for the model.

Then the AIC value of the model is the following. = − (^) Given a set of candidate models for the data, the preferred model is the one with the minimum AIC value. Sawa, Takamitsu, "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol.

46(6), pagesNovember. Full references (including those not matched with items on IDEAS). This paper deals with the bias corrections of two types of information criteria for selecting multivariate linear regression models in a general non-normal case.

One type is the AIC-type criterion. Sawa, T.,Information criteria for discriminating among alternative regression models, Econometr Google Scholar Cross Ref; Simes, R.J.,An improved Bonferroni procedure for multiple tests of significance, Biometr Google Scholar Cross Ref.

This book proposes a new methodology for the selection of one (model) from among a set of alternative econometric models. Let us recall that a model is an abstract representation of.

INFORMATION CRITERIA FOR DISCRIMINATING AMONG ALTERNATIVE REGRESSION MODELS BY TAKAMITSU SAWA' In recent years more and more emphasis has been placed on model discrimination procedures. In this paper we propose some new procedures for the selection of the most adequate regression model.

Properties of those procedures are analyzed and compared. date ﬁnal regression model with the forward selection, backward elimination and bidirec- Sawa, T.

Information criteria for discriminating among alternative regression models. Econometrica, 46(6), Schwarz, G. Estimating the dimension of a model. The best linear model can be obtained by stepwise regression analysis. Find an Information criteria for discriminating among alternative regression models.

Econometrica, 46(6), Schwarz, G. Estimating the dimension of a model. (). Information Criteria for Discriminating Among Alternative Regression Models, ().

Information Theory and an Extension of the Maximum Likelihood Principle. (). Likelihood Ratio-Tests for Model Selection and Non-Nested Hypotheses. ().Best subset selection. This function uses information criteria to find a specified number of best models containing one, two, or three variables, and so on, up to the single model .This paper chooses a Malaysian state in Borneo Island, Sarawak, as the case study to examine the relationship between population growth and economic development.

The findings imply that there is no statistically significant long-run relationship, but a causal relationship between population growth and economic development in Sarawak.

In other words, the empirical findings indicate that.