Robust nonparametric inference for the median

We consider the problem of constructing robust nonparametric confidence intervals and tests of hypothesis for the median when the data distribution is unknown and the data may contain a small fraction of contamination. We propose a modification of the sign test (and its associated confidence interval) which attains the nominal significance level (probability coverage) for any distribution in the contamination neighborhood of a continuous distribution. We also define some measures of robustness and efficiency under contamination for confidence intervals and tests. These measures are computed for the proposed procedures.Comment: Published at http://dx.doi.org/10.1214/009053604000000634 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The detection of influential subsets in linear regression using an influence matrix.

This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalues in this influence matrix. The method is illustrated with several well-known examples in the literature, and in all of them it succeeds in identifying the relevant influential subsets.Eigenvectors; Masking; Multivariate Influence; Outliers;

Adaptively truncated maximum likehood regression with asymmetric errors

We assume that the error model belongs to a location-scale family of distributions. Since in the asymmetric case the mean response is very often the parameter of interest and scale is a main component of mean, we do not assume that scale is a nuisance parameter. First, we show how to convert an ordinary robust estimate for the usual model with symmetric errors to an estimate for the more general model with asymmetric errors. Then, in order to improve efficiency, we introduce the truncated maximum likelihood or TML-estimator. A TML-estimate is computed in three steps: first, an initial high breakdown point estimate is computed; then, observations that are unlikely under the estimated model are rejected; finally, the maximum likelihood estimate is computed with the retained observations. The rejection rule used in the second step is based on a cut-off parameter that can be tuned to attain the desired efficiency while maintaining the breakdown point of the initial estimator (e.g., 50%). Optionally, one can use a new adaptive cut-off that, asymptotically, does not reject any observation when the data are generated according to the model. Under the model, the influence function of this adaptive TML-estimator (or ATML-estimator) coincides with the influence function of the maximum likelihood estimator. The ATML-estimator is, therefore, fully efficient at the model; nevertheless, its breakdown point is not smaller than the breakdown point of the initial estimator. We evaluate the TML- and ATML-estimators for finite sample sizes with the help of simulations and discuss an example with real data. [authors]]]> eng oai:serval.unil.ch:BIB_EC87BF75EEE8 2022-05-07T01:29:32Z <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> https://serval.unil.ch/notice/serval:BIB_EC87BF75EEE8 Organisational black holes Hameri, A.-P. info:eu-repo/semantics/conferenceObject inproceedings 2001-04 IT Strategy Summit, Scottsdale, Arizona, USA eng oai:serval.unil.ch:BIB_EC87F7DA08C2 2022-05-07T01:29:32Z <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> https://serval.unil.ch/notice/serval:BIB_EC87F7DA08C2 Monoclonal antibodies against recombinant-MAGE-1 protein identify a cross-reacting 72-kDa antigen which is co-expressed with MAGE-1 protein in melanoma cells info:doi:10.1002/(sici)1097-0215(19960729)67:3&lt;417::aid-ijc17&gt;3.0.co;2-4 info:eu-repo/semantics/altIdentifier/doi/10.1002/(sici)1097-0215(19960729)67:3&lt;417::aid-ijc17&gt;3.0.co;2-4 info:eu-repo/semantics/altIdentifier/pmid/8707418 Carrel, S. Schreyer, M. Spagnoli, G. Cerottini, J. C. Rimoldi, D. info:eu-repo/semantics/article article 1996-07 International Journal of Cancer, vol. 67, no. 3, pp. 417-22 info:eu-repo/semantics/altIdentifier/pissn/0020-7136 <![CDATA[The MAGE-1 gene codes for tumor-associated peptides recognized by cytolytic T lymphocytes in association with MHC-class-1 molecules such as HLA-A1 and HLA-Cw16. In the course of a study aiming at the immunohistochemical detection of the MAGE-1 gene product in tumor samples, 2 mouse monoclonal antibodies (MAbs) directed against a full-length recombinant MAGE-1 fusion protein were found to react strongly not only with the 46-kDa MAGE-1 protein, but also with a 72-kDa product in immunoblots of lysates obtained from several MAGE-1-mRNA-positive melanoma cell lines. Pre-incubation of the antibodies with the recombinant MAGE-1 fusion protein abolished their reactivity both with MAGE-1 protein and with the 72-kDa product, thus confirming the occurrence of antigenic determinant(s) shared by the 2 proteins. The 72-kDa protein is not an alternative product of MAGE-1, since it was still detected in lysates of a MAGE-1 loss variant derived from a MAGE-1-positive melanoma cell line. Moreover, the 72-kDa protein does not appear to be a product of the other members of the MAGE gene family known to be expressed in tumors (such as MAGE-2, -3, -4 and -12). Interestingly, expression of the 72-kDa protein was found to be correlated with that of MAGE-1 protein. Thus, in 30 tumor cell lines analyzed by immunoblotting and RT-PCR, the 72-kDa protein was never detected in MAGE-1-mRNA-negative cell lines, while it was co-expressed with MAGE-1 protein in 12 out of 15 cell lines expressing MAGE-1. Furthermore, the 72-kDa protein was detected in lysates of human testis, the only normal tissue known to express MAGE-1. Finally, treatment of MAGE-1-mRNA-negative cell lines with 5-Aza-2'-deoxycytidine, a hypomethylating agent known to induce MAGE-1 expression, resulted in the expression of the 72-kDa protein. Taken collectively, these findings suggest that expression of the gene encoding the 72-kDa protein identified in this study through antigenic determinant(s) shared with MAGE-1 protein is regulated in a way similar to that of MAGE-1