Author: George KapetaniosPublisher:
ISBN:
Category : Bootstrap (Statistics)
Languages : en
Pages : 0
Book Description
Non-nested Models and the Likelihood Ratio Statistic
Author: George KapetaniosPublisher:
ISBN:
Category : Bootstrap (Statistics)
Languages : en
Pages : 0
Book Description
Model Choice in Nonnested Families
Author: Basilio de Bragança PereiraPublisher: Springer
ISBN: 3662537362
Category : Mathematics
Languages : en
Pages : 105
Book Description
This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
Testing Non-nested Multilevel Models
Author: Andrew Lawrence MoskowitzPublisher:
ISBN:
Category :
Languages : en
Pages : 224
Book Description
Comparing theories represented by statistical models is central to psychological research. Historically, comparisons between so called "non-nested" models have been error prone in the absence of a null hypothesis test. Recent research by Levy and Hancock and Merkle, You, and Preacher has extended Vuong's Likelihood Ratio Test of non-nested models to Structural Equation Models (SEMs). A notable omission of recent work is the extension of Vuong's test to the case of multilevel regression- a common approach for modeling longitudinal or grouped data. This dissertation leverages the similarities between SEMs and multilevel models to extend Vuong's test to the multilevel framework. The logic of Vuong's test as it relates to multilevel regression was explored and a SAS macro developed to facilitate the comparison between two models known to be non-nested a priori. The ability of Vuong's test to select the true or "best" model was compared to that of information criteria in three simulation studies reflecting scenarios in which non-nestedness is commonly encountered in multilevel regression: non-nested covariate sets, level 1 residual covariance structures, and functional forms. Selection rates of the incorrect models were also examined. Vuong's test showed almost no incorrect model selection across all scenarios, although its power to select the correct model was generally modest. Model comparisons among information criteria tended to be more sensitive than Vuong's test but also selected the incorrect model more often. Finally, Vuong's test was applied to three real data sets comparing competing models in the same scenarios as the simulation studies. Implications and recommendations for use are discussed.
Likelihood Ratio Procedures for Comparing Non-nested, Possibly Incorrect Regressors
Author: David F. FindleyPublisher:
ISBN:
Category : Chi-square test
Languages : en
Pages : 140
Book Description
Applied work involving statistical modeling frequently leads to situations where models must be compared which are not related to one another by parameter restrictions. In such a situation, log-likelihood ratios of pairs of estimated models do not have a chi-square limiting distribution, and statisticians making model selection decisions frequently resort to rather complicated and subjective comparisons of residuals or other model artifacts to accomplish the selection. This paper gives some theoretical background for the use of the usual log-likelihood ratios for non-nested comparisons. The practical importance of this capability is magnified by the fact that maximized likelihood values are usually available from the software used for estimation. Thus comparisons can often be made quickly. This encourages inventiveness and experimentation by the modeler.
The Modified Cox Test for Non-nested Model Selection
Author: Mitchell WatnikA Bounded-size Likelihood Test for Non-nested Probabilistic Discrete Choice Models Estimated from Choice-based Samples
Author: Joel HorowitzPublisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 42
Book Description
The Likelihood Ratio Test as a Model Selection Criterion with an Application to Models of Female Labor Supply Behavior
Author: Jeffrey E. ZabelPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 246
Book Description
Non-nested Pretest Tests
Author: Leo MichelisPublisher: London, Ont. : Department of Economics, University of Western Ontario
ISBN:
Category : Econometric models
Languages : en
Pages : 36
Book Description
A Predictability Test for a Small Number of Nested Models
Author: Eleonora GranzieraPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
In this paper we introduce tests of Likelihood Ratio types for one sided multivariate hypothesis to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. We show that the limiting distributions of the test statistics are non standard. For critical values we consider two approaches: (i) boostrapping and (ii) simulations assuming normality of the mean square prediction error (MSPE) difference. The size and the power performance of the tests are compared via Monte Carlo experiments with two existing tests proposed in Hubrich and West (2010): a chi-squared test and the maximum of t-statistic test. We find that all tests are well sized for one step ahead forecasts; for multi-step forecasts the normal approximation delivers grossly oversized tests, while the bootstrap provides with smaller size distortions. The experiments on the power reveal that the chi-squared test performs last while the ranking between the likelihood-ratio type test and the max-t stat depends on the simulation settings. Last, we apply our test to draw conclusions about the predictive ability of a Phillips type curve for the US core inflation.
Interpreting Standard and Nonstandard Log-Linear Models
Author: Patrick MairPublisher: Waxmann Verlag
ISBN: 9783830966111
Category : Log-linear models
Languages : en
Pages : 168
Book Description
Log-linear models can be used to analyze the relationships among categorical variables. The nature of these relationships is described based on the interpretation. This framework includes the usual standard models, but also nonstandard and non-hierarchical models. Alexander von Eye, Michigan State University.