Information criteria for discriminating among alternative regression models

by Takamitsu Sawa

Publisher: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign in [Urbana]

Written in English
Cover of: Information criteria for discriminating among alternative regression models | Takamitsu Sawa
Published: Pages: 33 Downloads: 557
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Subjects:

  • Distribution (Probability theory),
  • Information theory

Edition Notes

Bibliography: leaf 33.

StatementTakamitsu Sawa
SeriesFaculty working papers -- no. 455, Faculty working papers -- no. 455.
The Physical Object
Pagination33 leaves ;
Number of Pages33
ID Numbers
Open LibraryOL24980422M
OCLC/WorldCa5086038

Exponentially Weighted Information Criteria for Selecting Among Forecasting Models Abstract Information criteria (IC) are often used to select between forecasting models. Commonly used criteria are Akaike’s IC and Schwarz’s Bayesian IC. They involve the sum of two terms: the model’s log likelihood and a penalty for the number of model. You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. $\begingroup$ Deciding the specific covariates to include in a model does commonly go by the term model selection and there are a number of books with model selection in the title that are primarily deciding what model covariates/parameters to include in the model. $\endgroup$ – Michael R. Chernick Aug 24 '12 at the other models in the book, a task that is facilitated by the authors’ new case2alt command. The authors address the conditional logit model (fitted by clogit), the alternative-specific multinomial probit model (fitted by asmprobit), and rank-ordered logistic regression model (fitted by rologit). My own lack of prior familiarity with.

The standardized regression model.. Thus far, the interpretation of the regression coefficients in a regression model has been couched in unstandardized or raw metric form. Many regression routines will also produce a version of the model in standardized form. The standardized regression model is what results when all variables are first standardized prior to estimation of the model by. The Significance of Racial Discrimination for African American Youth. We define racial discrimination as the behavioral manifestation of underlying prejudiced beliefs about African Americans (Jones, ), and a component of the broader societal, macro-level construct of this way, racial discrimination consists of behavioral practices that operate systematically to maintain a social. Probit and Logit Regression. The linear probability model has a major flaw: it assumes the conditional probability function to be linear. This does not restrict \(P(Y=1\vert X_1,\dots,X_k)\) to lie between \(0\) and \(1\).We can easily see this in our reproduction of Figure of the book: for \(P/I \ ratio \geq \), predicts the probability of a mortgage application denial to be. With regard to information criteria, here is what SAS says: "Note that information criteria such as Akaike's (AIC), Schwarz's (SC, BIC), and QIC can be used to compare competing nonnested models, but do not provide a test of the comparison. Consequently, they cannot indicate whether one model is significantly better than another.

ear regression but that they often employ estimates of the regression parameters that are alternatives to the traditional least squares estimates. After doing standard regression, we introduce binomial and binary regression. These methods include (regularized, nonparametric) logistic regression and support vector machines. Fi-. A distinction is made between specification tests and model selection procedures. For the first, particular emphasis is on tests for residual spatial autocorrelation, tests on common factors, and tests on non-nested hypotheses. For the second, attention is focused on information-theoretic criteria, Bayesian approaches, and heuristic procedures. brief contents contents preface part i the linear regression model chapter 1 what is econometrics? chapter 2 choosing estimators: intuition and monte carlo methods chapter 3 linear estimators and a gauss-markov theorem chapter 4 blue estimators for the slope and intercept of a straight line chapter 5 residuals chapter 6 multiple regression part ii specification and hypothesis testing chapter 7.

Information criteria for discriminating among alternative regression models by Takamitsu Sawa Download PDF EPUB FB2

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 Information criteria for discriminating among alternative regression models book 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. Causal model selection tests. The four models M 1, M 2, M 3, and M 4 (Figure 1) are used to derive intersection-union tests based on the application of six separate Vuong (or Clarke) tests comparing, namely, f 1 × f 2, f 1 × f 3, f 1 × f 4, f 2 × f 3, f 2 × f 4, and f 3 × f 4.

Sun et al. implicitly used intersection unions of Vuong’s tests to select among three nonnested models. Collier Books (), (), "Correcting for Heteroscedasticity with Heteroscedasticity Consistent Standard Errors in the Linear Regression Model: Small Sample Considerations," The American Statistician, (), "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, 46, – Schwarz, G.

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.

On model selection criteria in multimodel analysis On model selection criteria in multimodel analysis Ye, Ming; Meyer, Philip D.; Neuman, Shlomo P. Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions.

There has been a growing tendency to postulate several alternative hydrologic models. Including additional variables generally reduces RMSE and increases R2. Hence, these are not appropriate to help guide the model choice.

In the s, Hirotugu Akaike, the eminent Japanese statistician, deveoped a metric called AIC (Akaike’s Information Criteria) that penalizes adding terms to a model.

In the case of regression, AIC has the form.