A modified signed likelihood ratio test in elliptical structural models

Melo, Tatiane F. N.; Ferrari, Silvia L. P.

doi:10.1007/s10182-010-0123-4

Cited by 6 publications

(10 citation statements)

References 14 publications

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“…Most of the applications of Barndorff-Nielsen's adjustment concern exponential family models and transformation models (e.g. Wong & Wu, 2002, Larsen et al, 2002, Melo & Ferrari, 2010and Cortese & Ventura, 2013. Approximations to R * that avoid the need for sample space derivatives have been proposed by several authors.…”

Section: Adjusted Signed Likelihood Ratio Statisticsmentioning

confidence: 99%

Small-sample one-sided testing in extreme value regression models

Ferrari

Pinheiro

2015

AStA Adv Stat Anal

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We derive adjusted signed likelihood ratio statistics for a general class of extreme value regression models. The adjustments reduce the error in the standard normal approximation to the distribution of the signed likelihood ratio statistic. We use Monte Carlo simulations to compare the finite-sample performance of the different tests. Our simulations suggest that the signed likelihood ratio test tends to be liberal when the sample size is not large, and that the adjustments are effective in shrinking the size distortion. Two real data applications are presented and discussed.

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Section: Adjusted Signed Likelihood Ratio Statisticsmentioning

confidence: 99%

Small-sample one-sided testing in extreme value regression models

Ferrari

Pinheiro

2015

AStA Adv Stat Anal

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“…Generally speaking, inference procedures under complicated models are based on asymptotic arguments. However, as shown by Melo and Ferrari [18], first-order asymptotics under errors-invariables models may be inaccurate for small-and moderate-sized samples. With the purpose of improving the approximation of the distribution of the likelihood ratio statistic to its asymptotic reference chi-square distribution, Skovgaard [23] proposes a new test statistic when the interest lies in testing a parameter vector (see Section 3 for details).…”

Section: Introductionmentioning

confidence: 98%

Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error

Melo

Ferrari

Patriota

2013

Journal of Statistical Computation and Simulation

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In this paper, we develop modified versions of the likelihood ratio test for multivariate heteroskedastic errors-in-variables regression models. The error terms are allowed to follow a multivariate distribution in the elliptical class of distributions, which has the normal distribution as a special case. We derive the Skovgaard adjusted likelihood ratio statistics, which follow a chisquared distribution with a high degree of accuracy. We conduct a simulation study and show that the proposed tests display superior finite sample behavior as compared to the standard likelihood ratio test. We illustrate the usefulness of our results in applied settings using a data set from the WHO MONICA Project on cardiovascular disease.

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“…This methodology could offer parametric inference techniques suitable for several distributions. The Birnbaum-Saunders distribution [16] and elliptical structural models [17] are illustrative examples. This approach has also been employed in SAR data models.…”

Section: Introductionmentioning

confidence: 99%

Bias Correction and Modified Profile Likelihood Under the Wishart Complex Distribution

Nascimento

Frery

Cintra

2014

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper proposes improved methods for the maximum likelihood (ML) estimation of the equivalent number of looks L. This parameter has a meaningful interpretation in the context of polarimetric synthetic aperture radar (PolSAR) images. Due to the presence of coherent illumination in their processing, PolSAR systems generate images which present a granular noise called speckle. As a potential solution for reducing such interference, the parameter L controls the signal-noise ratio. Thus, the proposal of efficient estimation methodologies for L has been sought. To that end, we consider firstly that a PolSAR image is well described by the scaled complex Wishart distribution. In recent years, Anfinsen et al. derived and analyzed estimation methods based on the ML and on trace statistical moments for obtaining the parameter L of the unscaled version of such probability law. This paper generalizes that approach. We present the second-order bias expression proposed by Cox and Snell for the ML estimator of this parameter. Moreover, the formula of the profile likelihood modified by Barndorff-Nielsen in terms of L is discussed. Such derivations yield two new ML estimators for the parameter L, which are compared to the estimators proposed by Anfinsen et al.. The performance of these estimators is assessed by means of Monte Carlo experiments, adopting three statistical measures as comparison criterion: the mean square error, the bias, and the coefficient of variation. Equivalently to the simulation study, an application to actual PolSAR data concludes that the proposed estimators outperform all the others in homogeneous scenarios.

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A modified signed likelihood ratio test in elliptical structural models

Abstract: Elliptical distribution, Errors-in-variables model, Measurement error, Modified signed likelihood ratio statistic, Structural model,

Cited by 6 publications

References 14 publications

Small-sample one-sided testing in extreme value regression models

Small-sample one-sided testing in extreme value regression models

Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error

Bias Correction and Modified Profile Likelihood Under the Wishart Complex Distribution

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