34,864 research outputs found
The asymptotic relative efficiency of mixed statistical tests
Mixed statistical tests are described. It is shown that these tests have a much higher efficiency than conventionally used statistics such as the sign test and polarity coincidence correlation without the high operational complexity of the Wilcoxon, Mann-Whitney, Kendall\tau, or Fisher-Yates: Terry-Hoeffding tests
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
For data sets populated by a very well modeled process and by another process
of unknown probability density function (PDF), a desired feature when
manipulating the fraction of the unknown process (either for enhancing it or
suppressing it) consists in avoiding to modify the kinematic distributions of
the well modeled one. A bootstrap technique is used to identify sub-samples
rich in the well modeled process, and classify each event according to the
frequency of it being part of such sub-samples. Comparisons with general MVA
algorithms will be shown, as well as a study of the asymptotic properties of
the method, making use of a public domain data set that models a typical search
for new physics as performed at hadronic colliders such as the Large Hadron
Collider (LHC).Comment: 8 pages, 5 figures. Proceedings of the XIIth Quark Confinement and
Hadron Spectrum conference, 28/8-2/9 2016, Thessaloniki, Greec
Composite Likelihood Inference by Nonparametric Saddlepoint Tests
The 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 strenght 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. In this
paper it is shown how accurate inference can be obtained by using a fully
nonparametric saddlepoint test statistic derived from the composite score
functions. The proposed statistic is asymptotically chi-square 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
New L2-type exponentiality tests
We introduce new consistent and scale-free goodness-of-fit tests for the exponential distribution based on the Puri-Rubin characterization. For the construction of test statistics we employ weighted L2 distance between V-empirical Laplace transforms of random variables that appear in the characterization. We derive the asymptotic behaviour under the null hypothesis as well as under fixed alternatives. We compare our tests, in terms of the Bahadur efficiency, to the likelihood ratio test, as well as some recent characterization based goodness-of-fit tests for the exponential distribution. We also compare the power of our tests to the power of some recent and classical exponentiality tests. According to both criteria, our tests are shown to be strong and outperform most of their competitors.Peer Reviewe
Pranab Kumar Sen: Life and works
In this article, we describe briefly the highlights and various
accomplishments in the personal as well as the academic life of Professor
Pranab Kumar Sen.Comment: Published in at http://dx.doi.org/10.1214/193940307000000013 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
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