New Criteria of Model Selection and Model Averaging in Linear Regression Models

Haggag, Magda M. M.

doi:10.11648/j.ajtas.20140305.15

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…It suggested that the RUM specified with RDEU Quig-9 AIC = 2k -2 ln(LL), BIC = k ln(n) -2 ln(LL), HQC = 2k ln(ln(n)) -2 ln(LL); where k is the number of estimated parameters, n is the number of observations and LL is the maximised log-likelihood. AIC provides a simple approach and is widely used in practice among analysis of complex data but may not perform well if there are too many parameters, whereas BIC and HQC try to reduce the potential bias by imposing a more stringent penalty on the number of parameters than that of AIC (Haggag 2014). gin using the CARA utility function was the best fit for the data, according to the AIC, the BIC, and the HQC.…”

Section: Results and Analysesmentioning

confidence: 99%

Investigation of the Stochastic Choice under Risk using Experimental Data

Permana

Empel

Pradiptyo

2020

GADJAH MADA INT. J. BUS.

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This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice.

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Section: Results and Analysesmentioning

confidence: 99%

Investigation of the Stochastic Choice under Risk using Experimental Data

Permana

Empel

Pradiptyo

2020

GADJAH MADA INT. J. BUS.

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“…The HQC criterion does not exhibit asymptotically efficient criteria but also indicates higher consistency levels than AIC and BIC criteria (Haggag 2014). Besides, other statistical metrics, such as RMSE, MSE and MAE test, usually defines error statistics in the units of constituents of interests.…”

Section: Model Compatibility Investigationmentioning

confidence: 99%

A new nonparametric copula framework for the joint analysis of river temperature and low flow characteristics for aquatic habitat risk assessment

Latif

Ouarda

St‐Hilaire

et al. 2023

Preprint

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The joint probability analysis of river water temperature (RWT) and low flow (LF) characteristics is essential as their combined effect can negatively affect aquatic species, e.g., ectotherm fish. Traditional multivariate models may not be as effective as copula-based methodologies. This study introduces a new multivariate approach, the nonparametric copula density framework, free from any distribution assumption in their univariate margins and copula joint density. The proposed framework utilized RWT and LF datasets collected at five different river stations in Switzerland. The study evaluates a nonparametric Gaussian kernel with six bandwidth selectors to model marginal densities. It employs nonparametric-based Beta kernel density, Bernstein estimator, and Transformation kernel estimator to approximate copula density with nonparametric and parametric margins. The performance of some parametric copula densities was also compared. The most justifiable models were employed to estimate bivariate joint exceedance probabilities and return periods (RPs). The Beta kernel copula with Gaussian kernel margins outperformed other models for most stations; Bernstein and Transformation copula with Gaussian kernel margins were better for only one station each. The univariate RPs (RWT or LF) are lower than the AND-joint but higher than OR joint case. As the percentile value of LF events (serve as a conditioning variable) increases, the bivariate joint RPs of RWT also increase. Higher values in RWT events result in higher RPs than lower values at the fixed percentile value of LF. All such estimated risk statistics are beneficial to analyze their mutual risk in aquatic habitats and freshwater ecosystems.

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Nonparametric Approach to Copula Estimation in Compounding The Joint Impact of Storm Surge and Rainfall Events in Coastal Flood Analysis

Latif

Simonović́

2022

Water Resour Manage

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The joint probability modelling of storm surge and rainfall events is the main task in assessing compound flood risk in low-lying coastal areas. These extreme or non-extreme events may not be dangerous if considered individually but can intensify flooding impact if they occur simultaneously or successively. Recently, the copula approach has been widely accepted in compound flooding but is often limited to parametric, or in limited number of cases to semiparametric, distribution settings. However, both parametric and semiparametric approaches assume the prior distribution type for univariate marginals and copula joint density. In that case, there is a high risk of misspecification if the underlying assumption is violated. In addition, both approaches suffer from a lack of flexibility. This study uses bivariate copula density in the nonparametric distribution setting. The joint copula structure is approximated nonparametrically by employing Beta kernel and Bernstein copula estimators, and their performances are also compared. The proposed model is tested with 46 years of rainfall and storm surge observations collected on Canada's west coast. Based on the different model compatibility tests, the Bernstein copula with normal KDE margins defined the joint dependence structure well. The selected nonparametric copula model is further employed to estimate joint and conditional return periods.The derived model is further used to estimate failure probability statistics to assess the variation of bivariate hydrologic risk during the project lifetime due to compounded storm surge and rainfall events.

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New Criteria of Model Selection and Model Averaging in Linear Regression Models

Cited by 6 publications

References 28 publications

Investigation of the Stochastic Choice under Risk using Experimental Data

Investigation of the Stochastic Choice under Risk using Experimental Data

A new nonparametric copula framework for the joint analysis of river temperature and low flow characteristics for aquatic habitat risk assessment

Nonparametric Approach to Copula Estimation in Compounding The Joint Impact of Storm Surge and Rainfall Events in Coastal Flood Analysis

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