A Robust Consistent Information Criterion for Model Selection Based on Empirical Likelihood

AbstractWe introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

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Decline in body condition in the Antarctic minke whale (Balaenoptera bonaerensis) in the Southern Ocean during the 1990s

Polar Biology ◽

10.1007/s00300-020-02783-3 ◽

2021 ◽

Vol 44 (2) ◽

pp. 259-273

Author(s):

Céline Cunen ◽

Lars Walløe ◽

Kenji Konishi ◽

Nils Lid Hjort

Keyword(s):

Model Selection ◽

Body Condition ◽

Model Building ◽

Research Question ◽

Information Criterion ◽

The Body ◽

Antarctic Minke Whale ◽

Minke Whales ◽

Balaenoptera Bonaerensis ◽

The Antarctic

AbstractChanges in the body condition of Antarctic minke whales (Balaenoptera bonaerensis) have been investigated in a number of studies, but remain contested. Here we provide a new analysis of body condition measurements, with particularly careful attention to the statistical model building and to model selection issues. We analyse body condition data for a large number (4704) of minke whales caught between 1987 and 2005. The data consist of five different variables related to body condition (fat weight, blubber thickness and girth) and a number of temporal, spatial and biological covariates. The body condition variables are analysed using linear mixed-effects models, for which we provide sound biological motivation. Further, we conduct model selection with the focused information criterion (FIC), reflecting the fact that we have a clearly specified research question, which leads us to a clear focus parameter of particular interest. We find that there has been a substantial decline in body condition over the study period (the net declines are estimated to 10% for fat weight, 7% for blubber thickness and 3% for the girth). Interestingly, there seems to be some differences in body condition trends between males and females and in different regions of the Antarctic. The decline in body condition could indicate major changes in the Antarctic ecosystem, in particular, increased competition from some larger krill-eating whale species.

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The Optimal Selection for Restricted Linear Models with Average Estimator

Abstract and Applied Analysis ◽

10.1155/2014/692472 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13

Author(s):

Qichang Xie ◽

Meng Du

Keyword(s):

Model Selection ◽

Linear Models ◽

Weighted Average ◽

Selection Procedure ◽

Information Criterion ◽

Optimal Weights ◽

Model Average ◽

Squared Error ◽

Generalized Information Criterion ◽

Risk Investment

The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing ak-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.

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

Economies ◽

10.3390/economies8020049 ◽

2020 ◽

Vol 8 (2) ◽

pp. 49 ◽

Cited By ~ 1

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.

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Model Selection in Peptide-receptor Radionuclide Therapy for an Accurate Determination of Time Integrated Activity Coefficients

Journal of Physics Conference Series ◽

10.1088/1742-6596/2019/1/012079 ◽

2021 ◽

Vol 2019 (1) ◽

pp. 012079

Author(s):

N Atikah ◽

A Riana ◽

A Dwi ◽

Z Anwari ◽

Misrawati ◽

...

Keyword(s):

Model Selection ◽

Activity Coefficients ◽

Radionuclide Therapy ◽

Goodness Of Fit ◽

Accurate Determination ◽

Peptide Receptor Radionuclide Therapy ◽

Information Criterion ◽

Selection Function ◽

Pharmacokinetic Data ◽

Peptide Receptor

Abstract Calculation of accurate time-integrated activity coefficients (TIACs) is desirable in nuclear medicine dosimetry. The accuracy of the calculated TIACs is highly dependent on the fit function. However, systematic studies of determining a good function for peptide-receptor radionuclide therapy (PRRT) in different patients have not been reported in the literature. The aim of this study was to individually determine the best function for the calculation of TIACs in tumor and kidneys using a model selection based on the goodness of fit criteria and Corrected Akaike Information Criterion (AICc). The data used in this study was pharmacokinetic data of 111In-DOTATATE in tumor and kidneys obtained from 4 PRRT patients. Eleven functions with various parameterizations were formulated to describe the biokinetic data of 111In-DOTATATE in tumor and kidneys. The model selection was performed by choosing the best function from the function with sufficient goodness of fit based on the smallest AICc. Based on the results of model selection, function A 1 -(λ 1+λphys )t was selected as the best function for all tumor and kidneys in patients with meningioma tumors. By using this function, the calculated of TIACs could be more accurate for future patients with meningioma tumor.

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Using Model Selection Criteria to Choose the Number of Principal Components

Journal of Statistical Theory and Applications ◽

10.1007/s44199-021-00002-4 ◽

2021 ◽

Vol 20 (3) ◽

pp. 450-461

Author(s):

Stanley L. Sclove

Keyword(s):

Model Selection ◽

Principal Components ◽

Bayesian Information Criterion ◽

Selection Criteria ◽

Information Criterion ◽

Information Criteria ◽

Akaike's Information Criterion ◽

Model Selection Criteria ◽

Adequate Number ◽

Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

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