scholarly journals An Akaike-type information criterion for model selection under inequality constraints (pre-print)

2021 ◽  
Author(s):  
Rebecca M. Kuiper ◽  
Herbert Hoijtink
Keyword(s):  
Model Selection ◽  
Analysis Of Variance ◽  
Akaike Information Criterion ◽  
Inequality Constraints ◽  
Information Criterion ◽  
Order Restrictions ◽  
Population Means ◽  
Equality And Inequality Constraints ◽  
Order Restricted ◽  
The One

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

scholarly journals An Akaike-type information criterion for model selection under inequality constraints

Biometrika ◽  
2011 ◽  
Vol 98 (2) ◽  
pp. 495-501 ◽  
Author(s):  
R. M. Kuiper ◽  
H. Hoijtink ◽  
M. J. Silvapulle
Keyword(s):  
Model Selection ◽  
Analysis Of Variance ◽  
Akaike Information Criterion ◽  
Inequality Constraints ◽  
Information Criterion ◽  
Order Restrictions ◽  
Population Means ◽  
Equality And Inequality Constraints ◽  
Order Restricted ◽  
The One

Abstract The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

scholarly journals Generalization of the order-restricted information criterion for multivariate normal linear models (pre-print )

2021 ◽  
Author(s):  
Rebecca M. Kuiper ◽  
Herbert Hoijtink
Keyword(s):  
Model Selection ◽  
Analysis Of Variance ◽  
Linear Models ◽  
Inequality Constraints ◽  
Information Criterion ◽  
Variance Model ◽  
Order Restrictions ◽  
Population Means ◽  
Order Restricted ◽  
The One

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints.Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic.We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints.If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion.Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Second Order Segmented Polynomials for Syphilis and Gonorrhea Prevalence and Incidence Trends Estimation: Application to Spectrum’s Guinea-Bissau and South Africa Data

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Severin Guy Mahiane ◽  
Carel Pretorius ◽  
Eline Korenromp
Keyword(s):  
South Africa ◽  
Model Selection ◽  
Akaike Information Criterion ◽  
Recovery Rate ◽  
Time Trends ◽  
Information Criterion ◽  
Second Order ◽  
Incidence Trends ◽  
Guinea Bissau ◽  
Linear Regressions

Abstract This paper presents two approaches to smoothing time trends in prevalence and estimating the underlying incidence of remissible infections. In the first approach, we use second order segmented polynomials to smooth a curve in a bounded domain. In the second, incidence is modeled instead and the prevalence is reconstructed using the recovery rate which is assumed to be known. In both approaches, the number of knots and their positions are estimated, resulting in non-linear regressions. Akaike Information Criterion is used for model selection. The method is illustrated with Syphilis and Gonorrhea prevalence smoothing and incidence trend estimation in Guinea-Bissau and South Africa, respectively.

Sign in / Sign up

Export Citation Format

Share Document