1,740 research outputs found
On bias reduction and incidental parameters
No full textFirth (1993) introduced a method for reducing the bias of the maximum likelihood estimator. Here it is shown that the approach is also effective in reducing the sensitivity of inferential procedures to incidental parameters
Towards a unification of second-order theory for likelihood and marginal composite likelihood
No full textAn adjustment for marginal composite likelihoods is derived to match the second-order theory of the likelihood when inference is for a vector-valued parameter in the absence of nuisance components. The adjustment overcomes the failure of Bartlett identities for marginal composite likelihoods and leads to a Bartlett-correctable marginal composite likelihood ratio statistic
Comment on “Wang et al. (2005), Robust estimating functions and bias correction for longitudinal data analysis”
No full textThis note provides a discussion on the manuscript by Wang et al. (2005) who aim to robustify inference for longitudinal data analysis by replacing the ordinary generalized estimating function with an influence-bounded, possibly biased, version. To adjust for the bias of the ensuing robust estimator, the authors provide its analytic approximation by means of asymptotic expansions, and estimate it by plugging-in a nonrobust estimate of the parameter of interest. In this letter, we argue that the proposed bias-corrected estimator is, in fact, nonrobust
On the automorphism groups of Lunardon-Polverino scattered linear sets
No full textLunardon and Polverino introduced in 2001 a new family of maximum scattered
linear sets in to construct linear minimal R\'edei
blocking sets. This family has been extended first by Lavrauw, Marino,
Trombetti and Polverino in 2015 and then by Sheekey in 2016 in two different
contexts (semifields and rank metric codes). These linear sets are called
Lunardon-Polverino linear sets and this paper aims to determine their
automorphism groups, to solve the equivalence issue among Lunardon-Polverino
linear sets and to establish the number of inequivalent linear sets of this
family. We then elaborate on this number, providing explicit bounds and
determining its asymptotics.Comment: In the 2nd version, Eq.(15) in Theorem 3.2 has been corrected for
even q and odd
Composite likelihood inference by nonparametric saddlepoint tests
No full textThe class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strength of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite sample behavior of likelihood ratio, Wald, and score-type test statistics is tied to the Godambe information matrix. Consequently, inaccurate estimates of the Godambe information translate in inaccurate p-values. The approach based on a fully nonparametric saddlepoint test statistic derived from the composite score functions is shown to achieve accurate inference. The proposed statistic is asymptotically chi-squared distributed up to a relative error of second order and does not depend on the Godambe information. The validity of the method is demonstrated through simulation studies. © 2014 Elsevier B.V. All rights reserved
Comment on ‘Small sample GEE estimation of regression parameters for longitudinal data’
No full textIn longitudinal studies, the generalized estimating equation (GEE) estimator of the parameters of a marginal model is known to be consistent even if the working intra-subject covariance matrix is incorrectly specified. Recently, a small sample correction for the bias of the GEE estimator has been proposed. We show that this correction formula relies on the correct specification of the working intra-subject covariance matrix. We provide a revised formula that is valid under misspecification and develop the R package ‘BCgee’ to ease the practical use of the formula. Copyright © 2017 John Wiley & Sons, Ltd
ROSE: A Package for Binary Imbalanced Learning
No full textThe ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed
bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for
the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap or cross-validation methods
Second-order Accurate Confidence Regions Based on Members of the Generalized Power Divergence Family
No full textRecently, a technique based on pseudo-observations has been proposed to tackle the
so-called convex hull problem for the empirical likelihood statistic. The resulting adjusted empirical
likelihood also achieves the high-order precision of the Bartlett correction. Nevertheless, the tech-
nique induces an upper bound on the resulting statistic that may lead, in certain circumstances, to
worthless confidence regions equal to the whole parameter space. In this paper, we show that suit-
able pseudo-observations can be deployed to make each element of the generalized power divergence
family Bartlett-correctable and released from the convex hull problem. Our approach is conceived
to achieve this goal by means of two distinct sets of pseudo-observations with different tasks. An
important effect of our formulation is to provide a solution that permits to overcome the prob-
lem of the upper bound. The proposal, which effectiveness is confirmed by simulation results, gives
back attractiveness to a broad class of statistics that potentially contains good alternatives to the
empirical likelihood
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