800 research outputs found
Labour and Employment in a Globalising World. Autonomy, Collectives and Political Dilemmas.
This collection of essays provides new insight into the complex realities of labour and employment market globalisation. The pluridisciplinary and multi-faced understanding of globalisation is based upon ground research in ten countries from South to North. Its contextualisation of globalising labour and employment market, perceived as process, constitutes the originality of the book. Globalisation is understood through a single process of both standardisation and differentiation, which also underscores its political agenda. The globalising process incorporates trends of convergent and somewhat undifferentiated Southern and Northern situations in labour and employment. Strong political perspectives thereby emerge to help understand changes in current capitalism and question the longstanding North to South paradigm. As labour and employment markets standardise and differentiate, what other problematical threads can be pulled to strengthen the hypothesis that trends converge within a single globalising process? The comparative concepts and tools proposed in this volume help to answer these queries.Globalisation; Employment market; Labour market;
The Weight Function in the Subtree Kernel is Decisive
Tree data are ubiquitous because they model a large variety of situations,
e.g., the architecture of plants, the secondary structure of RNA, or the
hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data
is difficult per se. In this paper, we focus on the subtree kernel that is a
convolution kernel for tree data introduced by Vishwanathan and Smola in the
early 2000's. More precisely, we investigate the influence of the weight
function from a theoretical perspective and in real data applications. We
establish on a 2-classes stochastic model that the performance of the subtree
kernel is improved when the weight of leaves vanishes, which motivates the
definition of a new weight function, learned from the data and not fixed by the
user as usually done. To this end, we define a unified framework for computing
the subtree kernel from ordered or unordered trees, that is particularly
suitable for tuning parameters. We show through eight real data classification
problems the great efficiency of our approach, in particular for small
datasets, which also states the high importance of the weight function.
Finally, a visualization tool of the significant features is derived.Comment: 36 page
Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes
A piecewise-deterministic Markov process is a stochastic process whose
behavior is governed by an ordinary differential equation punctuated by random
jumps occurring at random times. We focus on the nonparametric estimation
problem of the jump rate for such a stochastic model observed within a long
time interval under an ergodicity condition. We introduce an uncountable class
(indexed by the deterministic flow) of recursive kernel estimates of the jump
rate and we establish their strong pointwise consistency as well as their
asymptotic normality. We propose to choose among this class the estimator with
the minimal variance, which is unfortunately unknown and thus remains to be
estimated. We also discuss the choice of the bandwidth parameters by
cross-validation methods.Comment: 36 pages, 18 figure
Multiple Testing and Variable Selection along Least Angle Regression's path
In this article, we investigate multiple testing and variable selection using
Least Angle Regression (LARS) algorithm in high dimensions under the Gaussian
noise assumption. LARS is known to produce a piecewise affine solutions path
with change points referred to as knots of the LARS path. The cornerstone of
the present work is the expression in closed form of the exact joint law of
K-uplets of knots conditional on the variables selected by LARS, namely the
so-called post-selection joint law of the LARS knots. Numerical experiments
demonstrate the perfect fit of our finding.
Our main contributions are three fold. First, we build testing procedures on
variables entering the model along the LARS path in the general design case
when the noise level can be unknown. This testing procedures are referred to as
the Generalized t-Spacing tests (GtSt) and we prove that they have exact
non-asymptotic level (i.e., Type I error is exactly controlled). In that way,
we extend a work from (Taylor et al., 2014) where the Spacing test works for
consecutive knots and known variance. Second, we introduce a new exact multiple
false negatives test after model selection in the general design case when the
noise level can be unknown. We prove that this testing procedure has exact
non-asymptotic level for general design and unknown noise level. Last, we give
an exact control of the false discovery rate (FDR) under orthogonal design
assumption. Monte-Carlo simulations and a real data experiment are provided to
illustrate our results in this case. Of independent interest, we introduce an
equivalent formulation of LARS algorithm based on a recursive function.Comment: 62 pages; new: FDR control and power comparison between Knockoff,
FCD, Slope and our proposed method; new: the introduction has been revised
and now present a synthetic presentation of the main results. We believe that
this introduction brings new insists compared to previous version
Integral estimation based on Markovian design
Suppose that a mobile sensor describes a Markovian trajectory in the ambient
space. At each time the sensor measures an attribute of interest, e.g., the
temperature. Using only the location history of the sensor and the associated
measurements, the aim is to estimate the average value of the attribute over
the space. In contrast to classical probabilistic integration methods, e.g.,
Monte Carlo, the proposed approach does not require any knowledge on the
distribution of the sensor trajectory. Probabilistic bounds on the convergence
rates of the estimator are established. These rates are better than the
traditional "root n"-rate, where n is the sample size, attached to other
probabilistic integration methods. For finite sample sizes, the good behaviour
of the procedure is demonstrated through simulations and an application to the
evaluation of the average temperature of oceans is considered.Comment: 45 page
Asymptotic formula for the tail of the maximum of smooth Stationary Gaussian fields on non locally convex sets
International audienceIn this paper we consider the distribution of the maximum of a Gaussian field defined on non locally convex sets. Adler and Taylor or AzaĂŻs and Wschebor give the expansions in the locally convex case. The present paper generalizes their results to the non locally convex case by giving a full expansion in dimension 2 and some generalizations in higher dimension. For a given class of sets, a Steiner formula is established and the correspondence between this formula and the tail of the maximum is proved. The main tool is a recent result of AzaĂŻs and Wschebor that shows that under some conditions the excursion set is close to a ball with a random radius. Examples are given in dimension 2 and higher
Power of the Spacing test for Least-Angle Regression
Recent advances in Post-Selection Inference have shown that conditional
testing is relevant and tractable in high-dimensions. In the Gaussian linear
model, further works have derived unconditional test statistics such as the
Kac-Rice Pivot for general penalized problems. In order to test the global
null, a prominent offspring of this breakthrough is the spacing test that
accounts the relative separation between the first two knots of the celebrated
least-angle regression (LARS) algorithm. However, no results have been shown
regarding the distribution of these test statistics under the alternative. For
the first time, this paper addresses this important issue for the spacing test
and shows that it is unconditionally unbiased. Furthermore, we provide the
first extension of the spacing test to the frame of unknown noise variance.
More precisely, we investigate the power of the spacing test for LARS and
prove that it is unbiased: its power is always greater or equal to the
significance level . In particular, we describe the power of this test
under various scenarii: we prove that its rejection region is optimal when the
predictors are orthogonal; as the level goes to zero, we show that the
probability of getting a true positive is much greater than ; and we
give a detailed description of its power in the case of two predictors.
Moreover, we numerically investigate a comparison between the spacing test for
LARS and the Pearson's chi-squared test (goodness of fit).Comment: 22 pages, 8 figure
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