A Study on Akaike’s Bayesian Information Criterion in Wave Estimation

2011 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Bayesian Modeling ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Response Functions ◽  
Local Minima ◽  
Normal Response ◽  
Wave Parameters ◽  
Optimum Estimates ◽  
Response Amplitude Operators ◽  
The Relationship

A feasibility study of Bayesian wave estimation was carried out to investigate the relationship between the minimum Akaike’s Bayesian information criterion (ABIC) and the estimated wave parameters. The ship response functions, which were used for the Bayesian wave estimation together with the ship motion cross spectra, were simply modified and compared with the normal response functions in connection with the accuracy of estimated wave parameters. Moreover, the concept of the ABIC surfaces was introduced to investigate the optimum estimates from the stochastic viewpoint and the physical viewpoint. As the result, it was revealed that the minimum ABIC did not always provide the best estimates from the viewpoint of wave estimation and the simply modified response functions could reduce the estimating errors in some cases. The reasons were considered that the estimating error at the sharp peak of response amplitude operators was closely related to existence of the local minima of the ABIC surface and the simply modified response functions had some effects to make the ABIC surface smoother. It is pointed out as the conclusion of this report that any estimating errors of the ship response functions were not considered in the Bayesian modeling.

An Improved Stochastic Modeling for Bayesian Wave Estimation

2012 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Stochastic Modeling ◽  
Bayesian Modeling ◽  
Response Functions ◽  
Modeling Procedure ◽  
Wave Parameters ◽  
The Relationship

A modified Bayesian modeling procedure for wave estimation is proposed. In this method, errors in the estimates of ship response functions can be taken into account. In order to discuss the relationship between the minimum ABIC and the accuracy of the estimated wave parameters, the ABIC surfaces and the optimum area of the wave estimation are shown with respect to the two hyperparameters. As a result, the modified Bayesian modeling makes the ABIC surface smoother and can provide stable wave estimation. This concludes that the modified Bayesian modeling is reliable within a certain accuracy to estimate the wave parameters.

Structural VARs, deterministic and stochastic trends: how much detrending matters for shock identification

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Varang Wiriyawit ◽  
Benjamin Wong
Keyword(s):  
Bayesian Information Criterion ◽  
Unit Roots ◽  
Information Criterion ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
Common Information ◽  
Stochastic Trends ◽  
Structural Vars ◽  
Structural Vector

AbstractDetrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition reveals biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend also distorts impulse response functions of even the correctly detrended variable within the SVAR system. Pretesting for unit roots mitigates trend misspecification to some extent. We also find that while practitioners can specify high lag order VARs to mitigate trend misspecification, relying on common information criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) may choose a lag order that is too low.

scholarly journals A copula-based quantifying of the relationship between race inequality among neighbourhoods in São Paulo and age at death

4open ◽  
2020 ◽  
Vol 3 ◽  
pp. 11 ◽  
Author(s):  
Verónica Andrea González-López ◽  
Rafael Rodrigues de Moraes
Keyword(s):  
Life Expectancy ◽  
Bayesian Information Criterion ◽  
Total Population ◽  
Information Criterion ◽  
Sao Paulo ◽  
São Paulo ◽  
Age At Death ◽  
Statistical Tools ◽  
Constant Rate ◽  
The Relationship

In this paper, we combine two statistical tools with the objective of creating models that represent the dependence between (i) the proportion of the black/brown population in relation to the total population of a neighborhood (pct) and (ii) the average age at which people died in the neighborhood (age). We explore the dependence between pct and age in São Paulo city, Brazil, during 2018. The statistical tools are models of copulas and informative and non-informative settings according to the Bayesian perspective. The different scenarios and models allow us to delineate the dependence between pct and age, and, through the Bayesian Information Criterion we can indicate which of these models best represents the data. The approach implemented here allows us to define estimates of variations in life expectancy conditioned by percentage intervals of pct. With them, we can conclude that on average all the scenarios point to a decrease in life expectancy by increasing the proportion of pct. When conditioning the percentages of pct to 4 intervals (0, 0.25], (0.25, 0.5], (0.5, 0.75], (0.75, 1] respectively, we note that the expectation is reduced in average at a constant rate from one interval in comparison with the immediate and next interval from left to right in [0, 1].

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

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 Prior-based Bayesian information criterion

2019 ◽  
Vol 3 (1) ◽  
pp. 2-13 ◽  
Author(s):  
M. J. Bayarri ◽  
James O. Berger ◽  
Woncheol Jang ◽  
Surajit Ray ◽  
Luis R. Pericchi ◽  
...  
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion
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