Matches in SemOpenAlex for { <https://semopenalex.org/work/W2738427460> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W2738427460 abstract "In this thesis we develop a method for efficient model building in nonlinear members of the GLM family, with emphasis on best subset selection and on utilization of existing linear regression software. The method is based on a estimator of a subset of regression parameters, under the assumption that the remaining parameters are zero. It has been introduced by Lawless and Singhal (1978) in a form which requires special software. We define an estimator which has the same functional form as the maximum likelihood estimator of regression parameters obtained from the IRLS procedure, and show that it is identical with the estimator proposed by Lawless and Singhal. The same estimator has been discussed by Hosmer, Jovanovic and Lemeshow (1989) in the context of best subset logistic regression, by Nordberg (1982) in the context of stepwise selection and by Gilks (1986) in a broader context of model selection. Asymptotic results are developed for quadratic forms, F-statistics and Mallows' C used in weighted linear regression best subset selection on the vector of pseudo-data. Based on these, practical guidelines for the use of the method in nonlinear GLM members are provided. It is shown how the linearized estimator can be used to obtain diagnostic measures and to estimate the bias in regression parameter estimates, for nonlinear GLM members, from existing linear regression software. Simulation results are provided for the logistic and Poisson regression models with uniformly distributed independent regressors, for sample sizes 100, 200 and 400. Simulation results closely follow theoretical results developed for quadratic forms, F-statistics and Mallows' C, and the upper percentiles of F-statistics are well approximated by the percentiles of the corresponding F distributions. Correction factors for the moments of the Pearson Chi-square statistic as discussed by McCullagh and Nelder (1989) are examined. Evidence shows that the correction factors depend on the true value of the underlying parameter but not on the sample size. Simulation results for the Poisson regression model Pearson Chi-square statistic show closer adherence to theoretical moments than they do for the logistic regression model." @default.
- W2738427460 created "2017-07-31" @default.
- W2738427460 creator A5042354936 @default.
- W2738427460 date "1991-05-01" @default.
- W2738427460 modified "2023-09-23" @default.
- W2738427460 title "Linearization, variable selection and diagnostics in generalized linear models" @default.
- W2738427460 hasPublicationYear "1991" @default.
- W2738427460 type Work @default.
- W2738427460 sameAs 2738427460 @default.
- W2738427460 citedByCount "1" @default.
- W2738427460 crossrefType "journal-article" @default.
- W2738427460 hasAuthorship W2738427460A5042354936 @default.
- W2738427460 hasConcept C105795698 @default.
- W2738427460 hasConcept C114494560 @default.
- W2738427460 hasConcept C120068334 @default.
- W2738427460 hasConcept C151730666 @default.
- W2738427460 hasConcept C151956035 @default.
- W2738427460 hasConcept C163175372 @default.
- W2738427460 hasConcept C185429906 @default.
- W2738427460 hasConcept C2779343474 @default.
- W2738427460 hasConcept C28826006 @default.
- W2738427460 hasConcept C32224588 @default.
- W2738427460 hasConcept C33923547 @default.
- W2738427460 hasConcept C41587187 @default.
- W2738427460 hasConcept C48921125 @default.
- W2738427460 hasConcept C86803240 @default.
- W2738427460 hasConcept C93959086 @default.
- W2738427460 hasConceptScore W2738427460C105795698 @default.
- W2738427460 hasConceptScore W2738427460C114494560 @default.
- W2738427460 hasConceptScore W2738427460C120068334 @default.
- W2738427460 hasConceptScore W2738427460C151730666 @default.
- W2738427460 hasConceptScore W2738427460C151956035 @default.
- W2738427460 hasConceptScore W2738427460C163175372 @default.
- W2738427460 hasConceptScore W2738427460C185429906 @default.
- W2738427460 hasConceptScore W2738427460C2779343474 @default.
- W2738427460 hasConceptScore W2738427460C28826006 @default.
- W2738427460 hasConceptScore W2738427460C32224588 @default.
- W2738427460 hasConceptScore W2738427460C33923547 @default.
- W2738427460 hasConceptScore W2738427460C41587187 @default.
- W2738427460 hasConceptScore W2738427460C48921125 @default.
- W2738427460 hasConceptScore W2738427460C86803240 @default.
- W2738427460 hasConceptScore W2738427460C93959086 @default.
- W2738427460 hasLocation W27384274601 @default.
- W2738427460 hasOpenAccess W2738427460 @default.
- W2738427460 hasPrimaryLocation W27384274601 @default.
- W2738427460 hasRelatedWork W1551816343 @default.
- W2738427460 hasRelatedWork W1580513926 @default.
- W2738427460 hasRelatedWork W1604185636 @default.
- W2738427460 hasRelatedWork W1969348870 @default.
- W2738427460 hasRelatedWork W2006717180 @default.
- W2738427460 hasRelatedWork W2013527206 @default.
- W2738427460 hasRelatedWork W2017614241 @default.
- W2738427460 hasRelatedWork W2026406072 @default.
- W2738427460 hasRelatedWork W2032276186 @default.
- W2738427460 hasRelatedWork W2090826008 @default.
- W2738427460 hasRelatedWork W2314066266 @default.
- W2738427460 hasRelatedWork W2792222694 @default.
- W2738427460 hasRelatedWork W2955073550 @default.
- W2738427460 hasRelatedWork W2963237203 @default.
- W2738427460 hasRelatedWork W2963294104 @default.
- W2738427460 hasRelatedWork W3122629389 @default.
- W2738427460 hasRelatedWork W3124265479 @default.
- W2738427460 hasRelatedWork W335628900 @default.
- W2738427460 hasRelatedWork W1855465517 @default.
- W2738427460 hasRelatedWork W2188521965 @default.
- W2738427460 isParatext "false" @default.
- W2738427460 isRetracted "false" @default.
- W2738427460 magId "2738427460" @default.
- W2738427460 workType "article" @default.