A wolf in sheep"s clothing: carnivory in dominant sea urchins in the Mediterranean

Arbacia lixula and Paracentrotus lividus are the dominant sea urchins in the Mediterranean sublittoral, where they are key structuring species due to their grazing activity. It has been commonly accepted that competition between both species is minimized by specializing in different algal foods. A. lixula is considered to feed mainly on encrusting coralline algae, while P. lividus prefers fleshy macroalgae. We used stable isotope analysis to test if these species occupy different trophic positions at 3 locations in the western Mediterranean and one in Macaronesia. Our results show unambiguously that A. lixula always occupies a higher trophic level than P. lividus, with a δ15N comparable in some locations to strict carnivores such as Actinia schmidti or Marthasterias glacialis. A temporal monitoring at one locality showed that this signature of a higher trophic level is consistent throughout the year. These results are incompatible with the current belief of an herbivorous diet for A. lixula and suggest that it must be considered an omnivore tending to carnivory in Mediterranean ecosystems, feeding at least partially on sessile animals such as Cirripedia, Hydrozoa or Bryozoa. A parallel analysis of gut contents showed a predominance of vegetal items in both species, although A. lixula consistently had a higher abundance of animal components than P. lividus. Our results challenge the validity of using gut content observations alone for characterizing the trophic behaviour of omnivorous marine invertebrates that feed on a variety of food sources with different digestibility.

Smith and Rawls share a room

We consider one-to-one matching (roommate) problems in which agents (students) can either be matched as pairs or remain single. The aim of this paper is twofold. First, we review a key result for roommate problems (the ``lonely wolf'' theorem) for which we provide a concise and elementary proof. Second, and related to the title of this paper, we show how the often incompatible concepts of stability (represented by the political economist Adam Smith) and fairness (represented by the political philosopher John Rawls) can be reconciled for roommate problems.

Anàlisi de la situació actual del llop (Canis lupus) a Catalunya i valoració socioambiental de la seva presència

En el present treball, en primer lloc, s’ha analitzat l’escenari actual en referència a la tornada del llop (Canis lupus) a Catalunya i s’han avaluat les mesures gestió que s’estan duent a terme al respecte, tot identificant els seus punts forts i els seus punts dèbils. D’aquesta manera, s’ha pogut saber que la població de llops actual és petita i poc estable, i es mou per la zona del Parc Natural del Cadí-Moixeró i els seus voltants, on s’estan efectuant les tasques de control i seguiment de l’espècie. No obstant, la seva evolució ha anat a l’alça durant els darrers anys, i es preveu que pugui colonitzar noves àrees a curt-mig termini, pel que aviat s’hauria d’ampliar l’àmbit d’aplicació d’aquesta gestió. Posteriorment, s’ha realitzat una valoració de la seva presència a Catalunya -encabida dins d’un marc hipotètic- per determinar l’ampli ventall d’aspectes socials i ambientals que envolten aquest fet. Mitjançant aquesta valoració s’han pogut detectar les principals mancances que presenta el país per fer front a aquesta situació, però també s’han identificat les fortaleses i les oportunitats que es presenten de cara al futur.

Model d'adequació de l'hàbitat del llop a Catalunya

Des de l’any 2000 es té constància de la presencia del llop a Catalunya. Des de llavors, com a mínim 14 llops diferents han entrat i sortit del territori català, encara que cap d’ells s’ha assentat de manera permanent. L’estudi analitza l’entorn català utilitzant GIS, creant un model d’adequació de l’hàbitat tenint en compte les següents variables: la distància a la carretera més propera, la biomassa disponible a la zona, l’altitud i el tipus i tant per cent de recobriment. El model es basa en la informació obtinguda mitjançant la consulta a experts tant del llop com del territori català, així com en una recerca bibliogràfica sobre l’adequació de l’hàbitat del llop. L’enquesta que es dirigí als experts té en compte els valors que cada variable pot prendre dins l’àrea d’estudi, estableix rangs dels valors de cada variable i pregunta als experts com cada rang pot afectar a l’adequació de l’hàbitat pel llop. Els resultats mostren com bona part de la zona Nord de Catalunya té unes condicions adequades perquè el llop pugui arribar a reproduir-s’hi. Es desenvolupa també una anàlisi dels possibles punts de conflicte humà-llop i una superposició dels espais protegits amb les zones més adequades per l’establiment del llop.

Stepwise multiple testing as formalized data snooping

It is common in econometric applications that several hypothesis tests arecarried out at the same time. The problem then becomes how to decide whichhypotheses to reject, accounting for the multitude of tests. In this paper,we suggest a stepwise multiple testing procedure which asymptoticallycontrols the familywise error rate at a desired level. Compared to relatedsingle-step methods, our procedure is more powerful in the sense that itoften will reject more false hypotheses. In addition, we advocate the useof studentization when it is feasible. Unlike some stepwise methods, ourmethod implicitly captures the joint dependence structure of the teststatistics, which results in increased ability to detect alternativehypotheses. We prove our method asymptotically controls the familywise errorrate under minimal assumptions. We present our methodology in the context ofcomparing several strategies to a common benchmark and deciding whichstrategies actually beat the benchmark. However, our ideas can easily beextended and/or modied to other contexts, such as making inference for theindividual regression coecients in a multiple regression framework. Somesimulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.

Improved nonparametric confidence intervals in time series regressions

Condence intervals in econometric time series regressions suffer fromnotorious coverage problems. This is especially true when the dependencein the data is noticeable and sample sizes are small to moderate, as isoften the case in empirical studies. This paper suggests using thestudentized block bootstrap and discusses practical issues, such as thechoice of the block size. A particular data-dependent method is proposedto automate the method. As a side note, it is pointed out that symmetricconfidence intervals are preferred over equal-tailed ones, since theyexhibit improved coverage accuracy. The improvements in small sampleperformance are supported by a simulation study.

Subsampling the mean of heavy-tailed dependent observations

We establish the validity of subsampling confidence intervals for themean of a dependent series with heavy-tailed marginal distributions.Using point process theory, we study both linear and nonlinear GARCH-liketime series models. We propose a data-dependent method for the optimalblock size selection and investigate its performance by means of asimulation study.

Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size

This paper analyzes whether standard covariance matrix tests work whendimensionality is large, and in particular larger than sample size. Inthe latter case, the singularity of the sample covariance matrix makeslikelihood ratio tests degenerate, but other tests based on quadraticforms of sample covariance matrix eigenvalues remain well-defined. Westudy the consistency property and limiting distribution of these testsas dimensionality and sample size go to infinity together, with theirratio converging to a finite non-zero limit. We find that the existingtest for sphericity is robust against high dimensionality, but not thetest for equality of the covariance matrix to a given matrix. For thelatter test, we develop a new correction to the existing test statisticthat makes it robust against high dimensionality.

Honey, I shrunk the sample covariance matrix

The central message of this paper is that nobody should be using the samplecovariance matrix for the purpose of portfolio optimization. It containsestimation error of the kind most likely to perturb a mean-varianceoptimizer. In its place, we suggest using the matrix obtained from thesample covariance matrix through a transformation called shrinkage. Thistends to pull the most extreme coefficients towards more central values,thereby systematically reducing estimation error where it matters most.Statistically, the challenge is to know the optimal shrinkage intensity,and we give the formula for that. Without changing any other step in theportfolio optimization process, we show on actual stock market data thatshrinkage reduces tracking error relative to a benchmark index, andsubstantially increases the realized information ratio of the activeportfolio manager.

Exact and approximate stepdown methods for multiple hypothesis testing

Consider the problem of testing k hypotheses simultaneously. In this paper,we discuss finite and large sample theory of stepdown methods that providecontrol of the familywise error rate (FWE). In order to improve upon theBonferroni method or Holm's (1979) stepdown method, Westfall and Young(1993) make eective use of resampling to construct stepdown methods thatimplicitly estimate the dependence structure of the test statistics. However,their methods depend on an assumption called subset pivotality. The goalof this paper is to construct general stepdown methods that do not requiresuch an assumption. In order to accomplish this, we take a close look atwhat makes stepdown procedures work, and a key component is a monotonicityrequirement of critical values. By imposing such monotonicity on estimatedcritical values (which is not an assumption on the model but an assumptionon the method), it is demonstrated that the problem of constructing a validmultiple test procedure which controls the FWE can be reduced to the problemof contructing a single test which controls the usual probability of a Type 1error. This reduction allows us to draw upon an enormous resamplingliterature as a general means of test contruction.

Improved estimation of the covariance matrix of stock returns with an application to portofolio selection

This paper proposes to estimate the covariance matrix of stock returnsby an optimally weighted average of two existing estimators: the samplecovariance matrix and single-index covariance matrix. This method isgenerally known as shrinkage, and it is standard in decision theory andin empirical Bayesian statistics. Our shrinkage estimator can be seenas a way to account for extra-market covariance without having to specifyan arbitrary multi-factor structure. For NYSE and AMEX stock returns from1972 to 1995, it can be used to select portfolios with significantly lowerout-of-sample variance than a set of existing estimators, includingmulti-factor models.

Flexible multivariate GARCH modeling with an application to international stock markets

The goal of this paper is to estimate time-varying covariance matrices.Since the covariance matrix of financial returns is known to changethrough time and is an essential ingredient in risk measurement, portfolioselection, and tests of asset pricing models, this is a very importantproblem in practice. Our model of choice is the Diagonal-Vech version ofthe Multivariate GARCH(1,1) model. The problem is that the estimation ofthe general Diagonal-Vech model model is numerically infeasible indimensions higher than 5. The common approach is to estimate more restrictive models which are tractable but may not conform to the data. Our contributionis to propose an alternative estimation method that is numerically feasible,produces positive semi-definite conditional covariance matrices, and doesnot impose unrealistic a priori restrictions. We provide an empiricalapplication in the context of international stock markets, comparing thenew estimator to a number of existing ones.

Subsampling inference in threshold autoregressive models

This paper discusses inference in self exciting threshold autoregressive (SETAR)models. Of main interest is inference for the threshold parameter. It iswell-known that the asymptotics of the corresponding estimator depend uponwhether the SETAR model is continuous or not. In the continuous case, thelimiting distribution is normal and standard inference is possible. Inthe discontinuous case, the limiting distribution is non-normal and cannotbe estimated consistently. We show valid inference can be drawn by theuse of the subsampling method. Moreover, the method can even be extendedto situations where the (dis)continuity of the model is unknown. In thiscase, also the inference for the regression parameters of the modelbecomes difficult and subsampling can be used advantageously there aswell. In addition, we consider an hypothesis test for the continuity ofthe SETAR model. A simulation study examines small sample performance.

Micro-Raman study of stress distribution in local isolation structures and correlation with transmission electron microscopy

Stress in local isolation structures is studied by micro‐Raman spectroscopy. The results are correlated with predictions of an analytical model for the stress distribution and with cross‐sectional transmission electron microscopy observations. The measurements are performed on structures on which the Si3N4 oxidation mask is still present. The influence of the pitch of the periodic local isolation pattern, consisting of parallel lines, the thickness of the mask, and the length of the bird"s beak on the stress distribution are studied. It is found that compressive stress is present in the Si substrate under the center of the oxidation mask lines, with a magnitude dependent on the width of the lines. Large tensile stress is concentrated under the bird"s beak and is found to increase with decreasing length of the bird"s beak and with increasing thickness of the Si3N4 film.

Dynamics of turing patterns under spatiotemporal forcing

We study, both theoretically and experimentally, the dynamical response of Turing patterns to a spatiotemporal forcing in the form of a traveling-wave modulation of a control parameter. We show that from strictly spatial resonance, it is possible to induce new, generic dynamical behaviors, including temporally modulated traveling waves and localized traveling solitonlike solutions. The latter make contact with the soliton solutions of Coullet [Phys. Rev. Lett. 56, 724 (1986)] and generalize them. The stability diagram for the different propagating modes in the Lengyel-Epstein model is determined numerically. Direct observations of the predicted solutions in experiments carried out with light modulations in the photosensitive chlorine dioxide-iodine-malonic acid reaction are also reported.