A Robust Version of Mallows's C p

Ronchetti, Elvezio; Staudte, Robert G.

doi:10.2307/2290858

Cited by 90 publications

(80 citation statements)

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“…2 , wi is a weight for i. th observation, andσ 2 is a robust and consistent estimator ofσ in the full model given byσ In robust Cp (RCP) criterion used by Ronchetti and Staudte (1994), Up-Vp value is constant for all models. In our study, the value of Up-Vp changes according to each subset.…”

Section: Model Selection Estimatorsmentioning

confidence: 99%

“…In our study, the value of Up-Vp changes according to each subset. Moreover, Ronchetti and Staudte (1994) have used weighting least squares (WLS) while computing the estimates. However, we use Huber-type estimation and Huber weights, instead of WLS.…”

Section: Model Selection Estimatorsmentioning

confidence: 99%

“…Hurvich and Tsai(1990) compared several model selection procedures for L1 regression. Ronchetti and Staudte (1994) proposed a robust version of Mallows's Cp. Sommer and Huggins (1996) proposed a robust Tp criterion based on Wald Statistics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust model selection criteria for robust S and LTS estimators

Çetin

2015

HJMS

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Outliers and multi-collinearity often have large influence in the model/variable selection process in linear regression analysis. To investigate this combined problem of multi-collinearity and outliers, we studied and compared Liu-type S (liuS-estimators) and Liu-type Least Trimmed Squares (liuLTS) estimators as robust model selection criteria. Therefore, the main goal this study is to select subsets of independent variables which explain dependent variables in the presence of multi-collinearity, outliers and possible departures from the normality assumption of the error distribution in regression analysis using these models. AMS Classification: 62F35

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Section: Model Selection Estimatorsmentioning

confidence: 99%

Section: Model Selection Estimatorsmentioning

confidence: 99%

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Robust model selection criteria for robust S and LTS estimators

Çetin

2015

HJMS

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“…One may use Akaike's information criterion (AIC) [1], the Bayesian information criterion (BIC) [19], Mallows's C p , [14], or its outlier robust version, [17], cross validation, [15], and its generalized version [10]. An extensive overview of most of the existing model selection techniques is given in [5].…”

Section: The Sparse Penalized Modelmentioning

confidence: 99%

A generalized quantile regression model

Nassiri

Loris

2013

Journal of Applied Statistics

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“…In order to choose k, or equivalently x 0 in a robust fashion, Dupuis and Victoria-Feser (2006) use another criterion, namely a prediction error criterion that is estimated robustly (see also Ronchetti and Staudte 1994), named the RC-criterion.…”

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confidence: 99%

Modelling Lorenz Curves: Robust and Semi-parametric Issues

Cowell

Victoria‐Feser

2008

Modeling Income Distributions and Lorenz Curves

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Modelling Lorenz curves (LC) for stochastic dominance comparisons is central to the analysis of income distribution. It is conventional to use non-parametric statistics based on empirical income cumulants which are in the construction of LC and other related second-order dominance criteria. However, although attractive because of its simplicity and its apparent flexibility, this approach suffers from important drawbacks. While no assumptions need to be made regarding the data-generating process (income distribution model), the empirical LC can be very sensitive to data particularities, especially in the upper tail of the distribution. This robustness problem can lead in practice to "wrong" interpretation of dominance orders. A possible remedy for this problem is the use of parametric or semi-parametric models for the datagenerating process and robust estimators to obtain parameter estimates. In this paper, we focus on the robust estimation of semi-parametric LC and investigate issues such as sensitivity of LC estimators to data contamination (Cowell and Victoria-Feser 2002), trimmed LC (Cowell and Victoria-Feser 2006) and inference for trimmed LC (Cowell and Victoria-Feser 2003), robust semi-parametric estimation for LC (Cowell and Victoria-Feser 2007) selection of optimal thresholds for (robust) semi-parametric modelling (Dupuis and Victoria-Feser 2006) and use both simulations and real data to illustrate these points.

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A Robust Version of Mallows's C p

Cited by 90 publications

References 0 publications

Robust model selection criteria for robust S and LTS estimators

Robust model selection criteria for robust S and LTS estimators

A generalized quantile regression model

Modelling Lorenz Curves: Robust and Semi-parametric Issues

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