scholarly journals Estimating Sparse Neuronal Signal from Hemodynamic Response: the Mixture Components Inference Approach

2019 ◽  
Author(s):  
Anna Pidnebesna ◽  
Iveta Fajnerová ◽  
Jiří Horáček ◽  
Jaroslav Hlinka
Keyword(s):  
Neuronal Activity ◽  
Information Criterion ◽  
Hemodynamic Response ◽  
Information Criteria ◽  
Statistical Testing ◽  
Sparse Regression ◽  
External Stimulation ◽  
Dantzig Selector ◽  
Magnetic Resonance Data ◽  
Novel Approach

AbstractThe approximate knowledge of the hemodynamic response to neuronal activity is widely used in statistical testing of effects of external stimulation, but has also been applied to estimate the neuronal activity directly from functional magnetic resonance data without knowing the stimulus timing. To this end, sparse linear regression methods have been previously used, including the well-known LASSO and the Dantzig selector. These methods generate a parametric family of solutions with different sparsity, among which a choice is finally based using some information criteria. As an alternative we propose a novel approach that instead utilizes the whole family of sparse regression solutions. Their ensemble provides a first approximation of probability of activation at each timepoint, and together with the conditional neuronal activity distributions estimated with the theory of mixtures with varying concentrations, they serve as the inputs to a Bayes classifier ultimately deciding between the true and false activations.As we show in extensive numerical simulations, the new method performs favourably in comparison with standard approaches in a range of realistic scenarios. This is mainly due to the avoidance of overfitting and underfitting that commonly plague the solutions based on sparse regression combined with model selection methods, including the corrected Akaike Information Criterion. This advantage is finally documented on fMRI task dataset.

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 Using Model Selection Criteria to Choose the Number of Principal Components

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.

scholarly journals Sensitivity and specificity of information criteria

2019 ◽  
Vol 21 (2) ◽  
pp. 553-565 ◽  
Author(s):  
John J Dziak ◽  
Donna L Coffman ◽  
Stephanie T Lanza ◽  
Runze Li ◽  
Lars S Jermiin
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
The Other ◽  
Biological Research ◽  
Ratio Test ◽  
Relative Importance ◽  
Fields Of Study ◽  
Alternative Perspective ◽  
Practical Implications

Abstract Information criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

A penalized regression approach for DNA copy number study using the sequencing data

2019 ◽  
Vol 18 (4) ◽  
Author(s):  
Jaeeun Lee ◽  
Jie Chen
Keyword(s):  
Copy Number ◽  
Information Criterion ◽  
Penalized Regression ◽  
Parameter Selection ◽  
Information Criteria ◽  
Tuning Parameter ◽  
Dimensional Structure ◽  
Sequencing Data ◽  
Dna Copy Number ◽  
Regression Approach

Abstract Modeling the high-throughput next generation sequencing (NGS) data, resulting from experiments with the goal of profiling tumor and control samples for the study of DNA copy number variants (CNVs), remains to be a challenge in various ways. In this application work, we provide an efficient method for detecting multiple CNVs using NGS reads ratio data. This method is based on a multiple statistical change-points model with the penalized regression approach, 1d fused LASSO, that is designed for ordered data in a one-dimensional structure. In addition, since the path algorithm traces the solution as a function of a tuning parameter, the number and locations of potential CNV region boundaries can be estimated simultaneously in an efficient way. For tuning parameter selection, we then propose a new modified Bayesian information criterion, called JMIC, and compare the proposed JMIC with three different Bayes information criteria used in the literature. Simulation results have shown the better performance of JMIC for tuning parameter selection, in comparison with the other three criterion. We applied our approach to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL 1954 and the results are in-line with those discovered in the literature.

scholarly journals Estimation of Scintillation Indices: A Novel Approach Based on Local Kernel Regression Methods

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammed Ouassou ◽  
Oddgeir Kristiansen ◽  
Jon G. O. Gjevestad ◽  
Knut Stanley Jacobsen ◽  
Yngvild L. Andalsvik
Keyword(s):  
Information Criteria ◽  
Ionospheric Scintillation ◽  
Local Regression ◽  
Regression Methods ◽  
Novel Approach ◽  
Low Pass ◽  
Discontinuous Phase ◽  
Novel Method ◽  
Highly Correlated ◽  
Scintillation Indices

We present a comparative study of computational methods for estimation of ionospheric scintillation indices. First, we review the conventional approaches based on Fourier transformation and low-pass/high-pass frequency filtration. Next, we introduce a novel method based on nonparametric local regression with bias Corrected Akaike Information Criteria (AICC). All methods are then applied to data from the Norwegian Regional Ionospheric Scintillation Network (NRISN), which is shown to be dominated by phase scintillation and not amplitude scintillation. We find that all methods provide highly correlated results, demonstrating the validity of the new approach to this problem. All methods are shown to be very sensitive to filter characteristics and the averaging interval. Finally, we find that the new method is more robust to discontinuous phase observations than conventional methods.

scholarly journals Anpassung eines Fixed Regression Modells für die tägliche Zunahme von Fleckviehbullen mit Hilfe von Informationskriterien

2007 ◽  
Vol 50 (1) ◽  
pp. 47-58
Author(s):  
N. Mielenz ◽  
H. Krejčová ◽  
J. Přibyl ◽  
L. Schüler
Keyword(s):  
Fixed Effects ◽  
Covariance Structure ◽  
Information Criterion ◽  
Complex Model ◽  
Second Order ◽  
Information Criteria ◽  
Covariance Structures ◽  
Expected Gain ◽  
Bayesian Criteria ◽  
Daily Gain

Abstract. Title of the paper: Fitting a fixed regression model for daily gain of bulls using information criterion In this study the model choice is demonstrated exemplarily on data of 6405 Czech Simmental bulls using information criterion. Per bull up to 8 observations were available for the trait daily gain. Because the animals showed different age on control day, the expected gain curves were described in the population and within the herd*year*season-classes by second, third or fourth order Legendre polynomials of age. For optimization of the fixed effects and to choice the covariance structure of the repeated records the information criteria of Akaike (AIC), the Bayesian criteria (BIC) and the ICOMP-criteria, developed mainly from Bozdogan, were used. Within and over all covariance structures AIC selected generally the most complex model. On the other hand, BIC and ICOMP favoured a model with second order polynomials of age nested within the head*year*seasonclasses. All criterion selected models with nested second order polynomials within the herd*year*season-classes in comparison to models with non-nested polynomials of age.

scholarly journals دراسة مقارنة لمعايير المعلومات لتحديد رتبة نماذج الانحدار الذاتي

2012 ◽  
Vol 18 (65) ◽  
pp. 323
Author(s):  
جنان عباس ناصر
Keyword(s):  
Autoregressive Model ◽  
Error Term ◽  
Information Criterion ◽  
Information Criteria ◽  
Autoregressive Models ◽  
Sample Sizes ◽  
Data Generating Process ◽  
The Mean ◽  
Normally Distributed

In this study, we compare between the traditional Information Criteria (AIC, SIC, HQ, FPE) with The Modified Divergence Information Criterion (MDIC) which used to determine the order of Autoregressive model (AR) for the data generating process, by using the simulation by generating data from several of Autoregressive models, when the error term is normally distributed with different values for its parameters (the mean and the variance),and for different sample  sizes.

scholarly journals On the Use of Information Criteria for Model Selection in Phylogenetics

2019 ◽  
Vol 37 (2) ◽  
pp. 549-562 ◽  
Author(s):  
Edward Susko ◽  
Andrew J Roger
Keyword(s):  
Model Selection ◽  
Bayes Factor ◽  
Information Criterion ◽  
Information Criteria ◽  
Leibler Divergence ◽  
Incorrect Model ◽  
Substantial Bias ◽  
And Performance ◽  
Phylogenetic Models ◽  
Selection Of

Abstract The information criteria Akaike information criterion (AIC), AICc, and Bayesian information criterion (BIC) are widely used for model selection in phylogenetics, however, their theoretical justification and performance have not been carefully examined in this setting. Here, we investigate these methods under simple and complex phylogenetic models. We show that AIC can give a biased estimate of its intended target, the expected predictive log likelihood (EPLnL) or, equivalently, expected Kullback–Leibler divergence between the estimated model and the true distribution for the data. Reasons for bias include commonly occurring issues such as small edge-lengths or, in mixture models, small weights. The use of partitioned models is another issue that can cause problems with information criteria. We show that for partitioned models, a different BIC correction is required for it to be a valid approximation to a Bayes factor. The commonly used AICc correction is not clearly defined in partitioned models and can actually create a substantial bias when the number of parameters gets large as is the case with larger trees and partitioned models. Bias-corrected cross-validation corrections are shown to provide better approximations to EPLnL than AIC. We also illustrate how EPLnL, the estimation target of AIC, can sometimes favor an incorrect model and give reasons for why selection of incorrectly under-partitioned models might be desirable in partitioned model settings.

scholarly journals Random Forest of Perfect Trees: Concept, Performance, Applications, and Perspectives

2021 ◽  
Author(s):  
Jean-Michel Nguyen ◽  
Pascal Jézéquel ◽  
Pierre Gillois ◽  
Luisa Silva ◽  
Faouda Ben Azzouz ◽  
...  
Keyword(s):  
Random Forest ◽  
Information Criterion ◽  
R Package ◽  
Information Criteria ◽  
Three Dimensions ◽  
Supplementary Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Classification Errors ◽  
New Type

Abstract Motivation The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman’s principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions. Results The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines—recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors. Availability and implementation The methods used to build the perfect trees in this article were implemented in the “ROP” R package, archived at https://cran.r-project.org/web/packages/ROP/index.html Supplementary information Supplementary data are available at Bioinformatics online.

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