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Semiconductor nanowires are believed to play a crucial role for future applications in electronics, spintronics and quantum technologies. A potential candidate is HgTe but its sensitivity to nanofabrication processes restrain its development. A way to circumvent this obstacle is the selective area growth technique. Here, in-plane HgTe nanostructures are grown thanks to selective area molecular beam epitaxy on a semi-insulating CdTe substrate covered with a patterned SiO$_{\mathrm{2}}$ mask. The shape of these nanostructures is defined by the in-plane orientation of the mask aperture along the <$110$>, <$1\bar{\mathrm{1}}0$>, or <$100$> direction, the deposited thickness, and the growth temperature. Several micron long in-plane nanowires can be achieved as well as more complex nanostructures such as networks, diamond structures or rings. A good selectivity is achieved with very little parasitic growth on the mask even for a growth temperature as low as $140${\deg}C and growth rate up to $0.5$ ML/s. For <$110$> oriented nanowires, the center of the nanostructure exhibits a trapezoidal shape with {$111$}B facets and two grains on the sides, while <$1\bar{\mathrm{1}}0$> oriented nanowires show {$111$}A facets with adatoms accumulation on the sides of the top surface. Transmission electron microscopy observations reveal a continuous epitaxial relation between the CdTe substrate and the HgTe nanowire. Measurements of the resistance with fourpoint scanning tunneling microscopy indicates a good electrical homogeneity along the main NW axis and a thermally activated transport. This growth method paves the way toward the fabrication of complex HgTe-based nanostructures for electronic transport measurements., Comment: 18 pages and 8 figures. Submitted to Nanotechnology

We report on the structural, electric and magnetic properties of $(\mathrm{Ni}_x\mathrm{Co}_{1-x})\mathrm{B}$ ferromagnetic nanotubes, displaying azimuthal magnetization. The tubes are fabricated using electroless plating in polycarbonate porous templates, with lengths several tens of micrometers, diameters from 100nm to 500nm and wall thicknesses from 10nm to 80nm. The resistivity is $\sim 1.5\times10^{-6}\mathrm{\Omega/m}$, and the anisotropic magnetoresistance~(AMR) of 0.2-0.3%, one order of magnitude larger~(resp. smaller) than in the bulk material, which we attribute to the resistance at grain boundaries. We determined the azimuthal anisotropy field from M(H) AMR loops of single tubes contacted electrically. Its magnitude is around 10mT, and tends to increase with the tube wall thickness, as well as the Co content. However, surprisingly it does not dependent much on the diameter nor on the curvature., Comment: 12 pages, 10 figures, includes methods and appendix

We propose and implement an approach to inference in linear instrumental variables models which is simultaneously robust and computationally tractable. Inference is based on self-normalization of sample moment conditions, and allows for (but does not require) many (relative to the sample size), weak, potentially invalid or potentially endogenous instruments, as well as for many regressors and conditional heteroskedasticity. Our coverage results are uniform and can deliver a small sample guarantee. We develop a new computational approach based on semidefinite programming, which we show can equally be applied to rapidly invert existing tests (e.g,. AR, LM, CLR, etc.).

This paper studies point identification of the distribution of the coefficients in some random coefficients models with exogenous regressors when their support is a proper subset, possibly discrete but countable. We exhibit trade-offs between restrictions on the distribution of the random coefficients and the support of the regressors. We consider linear models including those with nonlinear transforms of a baseline regressor, with an infinite number of regressors and deconvolution, the binary choice model, and panel data models such as single-index panel data models and an extension of the Kotlarski lemma.

This paper considers linear panel data models where the dependence of the regressors and the unobservables is modelled through a factor structure. The asymptotic setting is such that the number of time periods and the sample size both go to infinity. Non-strong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations.

This paper considers endogenous selection models, in particular nonparametric ones. Estimating the unconditional law of the outcomes is possible when one uses instrumental variables. Using a selection equation which is additively separable in a one dimensional unobservable has the sometimes undesirable property of instrument monotonicity. We present models which allow for nonmonotonicity and are based on nonparametric random coefficients indices. We discuss their nonparametric identification and apply these results to inference on nonlinear statistics such as the Gini index in surveys when the nonresponse is not missing at random.

The Fourier transform truncated on [-c,c] is usually analyzed when acting on L^2(-1/b,1/b) and its right-singular vectors are the prolate spheroidal wave functions. This paper considers the operator acting on the larger space L^2(exp(b|.|)) on which it remains injective. We give nonasymptotic upper and lower bounds on the singular values with similar qualitative behavior in m (the index), b, and c. The lower bounds are used to obtain rates of convergence for stable analytic continuation of possibly nonbandlimited functions whose Fourier transform belongs to L^2(exp(b|.|)). We also derive bounds on the sup-norm of the singular functions. Finally, we propose a numerical method to compute the SVD and apply it to stable analytic continuation when the function is observed with error on an interval.

We consider a linear model where the coefficients - intercept and slopes - are random with a law in a nonparametric class and independent from the regressors. Identification often requires the regressors to have a support which is the whole space. This is hardly ever the case in practice. Alternatively, the coefficients can have a compact support but this is not compatible with unbounded error terms as usual in regression models. In this paper, the regressors can have a support which is a proper subset but the slopes (not the intercept) do not have heavy-tails. Lower bounds on the supremum risk for the estimation of the joint density of the random coefficients density are obtained for a wide range of smoothness, where some allow for polynomial and nearly parametric rates of convergence. We present a minimax optimal estimator, a data-driven rule for adaptive estimation, and made available a R package.

This paper considers a nuclear norm penalized estimator for panel data models with interactive effects. The low-rank interactive effects can be an approximate model and the rank of the best approximation unknown and grow with sample size. The estimator is solution of a well-structured convex optimization problem and can be solved in polynomial-time. We derive rates of convergence, study the low-rank properties of the estimator, estimation of the rank and of annihilator matrices when the number of time periods grows with the sample size. Two-stage estimators can be asymptotically normal. None of the procedures require knowledge of the variance of the errors.

This was a revision of arXiv:1105.2454v1 from 2012. It considers a variation on the STIV estimator where, instead of one conic constraint, there are as many conic constraints as moments (instruments) allowing to use more directly moderate deviations for self-normalized sums. The idea first appeared in formula (6.5) in arXiv:1105.2454v1 when some instruments can be endogenous. For reference and to avoid confusion with the STIV estimator, this estimator should be called C-STIV.

We report the imaging of magnetic domains in ferromagnetic CoNiB nanotubes with very long aspect ratio, fabricated by electroless plating. While axial magnetization is expected for long tubes made of soft magnetic materials, we evidence series of azimuthal domains. We tentatively explain these by the interplay of anisotropic strain and/or grain size, with magneto-elasticity and/or anisotropic interfacial magnetic anisotropy. This material could be interesting for dense data storage, as well as curvature-induced magnetic phenomena such as the non-reciprocity of spin-wave propagation., Comment: Focus only on azimuthal domains, information on domain walls (part of v1) removed - title, abstract and discussion changed accordingly, tubular racetracks scheme removed; new references, figures. Submission to SciPost (reformatted, new abstract, DOI added to references). Enhanced discussion of anisotropy, disagreement of synchrotron data and magnetometry rectified, new MOKE data added

We introduce a method which enables to directly compare the impact of elastic strain on the optical properties of distinct quantum dots (QDs). Specifically, the QDs are integrated in a cross-section of a semiconductor core wire which is surrounded by an amorphous straining shell. Detailed numerical simulations show that, thanks to the mechanical isotropy of the shell, the strain field in a core section is homogeneous. Furthermore, we use the core material as an in situ strain gauge, yielding reliable values for the emitter energy tuning slope. This calibration technique is applied to self-assembled InAs QDs submitted to incremental tensile strain along their growth axis. In contrast to recent studies conducted on similar QDs stressed perpendicularly to their growth axis, optical spectroscopy reveals 5-10 times larger tuning slopes, with a moderate dispersion. These results highlight the importance of the stress direction to optimise QD response to applied strain, with implications both in static and dynamic regimes. As such, they are in particular relevant for the development of wavelength-tunable single photon sources or hybrid QD opto-mechanical systems.

We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its numerical implementation is faster than for previously known techniques using conic programs, and it allows one to deal with higher dimensional models. We provide upper bounds for estimation and prediction errors of the proposed estimator showing that it achieves the same rate as in the more restrictive situation of fixed design and i.i.d. Gaussian errors with known variance. Following Gautier and Tsybakov (2011), we obtain the results under weaker sensitivity assumptions than the restricted eigenvalue or assimilated conditions.

This paper considers treatment effects under endogeneity with complex heterogeneity in the selection equation. We model the outcome of an endogenous treatment as a triangular system, where both the outcome and first-stage equations consist of a random coefficients model. The first-stage specifically allows for nonmonotone selection into treatment. We provide conditions under which marginal distributions of potential outcomes, average and quantile treatment effects, all conditional on first-stage random coefficients, are identified. Under the same conditions, we derive bounds on the (conditional) joint distributions of potential outcomes and gains from treatment, and provide additional conditions for their point identification. All conditional quantities yield unconditional effects (\emph{e.g.}, the average treatment effect) by weighted integration.

We consider the estimation of wealth inequality measures with their confidence interval, based on survey data with interval censoring. We rely on a Bayesian hierarchical model. It consists of a model where, due to survey sampling and unit nonresponse, the summaries of the wealth distribution of households are observed with error; a mixture of multivariate models for the wealth components where groups correspond to portfolios of assets; and a prior on the parameters. A Gibbs sampler is used for numerical purposes to do the inference. We apply this strategy to the French 2004 Wealth Survey. In order to alleviate the nonresponse, the amounts were systematically collected in the form of brackets. Matched administrative data on the liability of the respondents for wealth tax and response to overview questions are used to better localize the wealth components. It implies nonrectangular multidimensional censoring. The variance of the error term in the model for the population inequality measures is obtained using linearization and taking into account the complex sampling design and the various weight adjustments., Comment: Published in at http://dx.doi.org/10.1214/10-AOAS443 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

In the random coefficients binary choice model, a binary variable equals 1 iff an index $X^\top\beta$ is positive.The vectors $X$ and $\beta$ are independent and belong to the sphere $\mathbb{S}^{d-1}$ in $\mathbb{R}^{d}$.We prove lower bounds on the minimax risk for estimation of the density $f\_{\beta}$ over Besov bodies where the loss is a power of the $L^p(\mathbb{S}^{d-1})$ norm for $1\le p\le \infty$. We show that a hard thresholding estimator based on a needlet expansion with data-driven thresholds achieves these lower bounds up to logarithmic factors.

This article considers inference in linear instrumental variables models with many regressors, all of which could be endogenous. We propose the STIV estimator. Identification robust confidence sets are derived by solving linear programs. We present results on rates of convergence, variable selection, confidence sets which adapt to the sparsity, and analyze confidence bands for vectors of linear functions using bias correction. We also provide solutions to some instruments being endogenous. The application is to the EASI demand system.

Nous consid\'erons dans cet article des mod\`eles \`a choix binaires et coefficients al\'eatoires. Le but est d'estimer de mani\`ere nonparam\'etrique la densit\'e du coefficient al\'eatoire. Il s'agit d'un probl\`eme inverse mal pos\'e caract\'eris\'e par une transformation int\'egrale. Un nouvel estimateur de la densit\'e du coefficient al\'eatoire est propos\'e. Il est bas\'e sur les d\'eveloppements en s\'eries de Fourier-Laplace sur la sph\`ere. Cette approche permet une \'etude fine du probl\`eme d'identification mais aussi d'obtenir un estimateur par injection ayant une expression explicite et ne n\'ecessitant aucun optimisation num\'erique. Le nouvel estimateur est donc tr\`es facile \`a obtenir num\'eriquement, tout en \'etant souple sur le traitement de l'h\'et\'erog\'en\'eit\'e inobserv\'ee. Nous pr\'esentons des extensions parmi lesquellesle traitement de coefficients non al\'eatoires et de mod\`eles avec endog\'en\'eit\'e.

Synthetic indices are used in Economics to measure various aspects of monetary inequalities. These scalar indices take as input the distribution over a finite population, for example the population of a specific country. In this article we consider the case of the French 2004 Wealth survey. We have at hand a partial measurement on the distribution of interest consisting of bracketed and sometimes missing data, over a subsample of the population of interest. We present in this article the statistical methodology used to obtain point and interval estimates taking into account the various uncertainties. The inequality indices being nonlinear in the input distribution, we rely on a simulation based approach where the model for the wealth per household is multivariate. Using the survey data as well as matched auxiliary tax declarations data, we have at hand a quite intricate non-rectangle multidimensional censoring. For practical issues we use a Bayesian approach. Inference using Monte-Carlo approximations relies on a Monte-Carlo Markov chain algorithm namely the Gibbs sampler. The quantities interesting to the decision maker are taken to be the various inequality indices for the French population. Their distribution conditional on the data of the subsample are assumed to be normal centered on the design-based estimates with variance computed through linearization and taking into account the sample design and total nonresponse. Exogeneous selection of the subsample, in particular the nonresponse mechanism, is assumed and we condition on the adequate covariates.

We consider two exit problems for the Korteweg-de Vries equation perturbed by an additive white in time and colored in space noise of amplitude a. The initial datum gives rise to a soliton when a=0. It has been proved recently that the solution remains in a neighborhood of a randomly modulated soliton for times at least of the order of a^{-2}. We prove exponential upper and lower bounds for the small noise limit of the probability that the exit time from a neighborhood of this randomly modulated soliton is less than T, of the same order in a and T. We obtain that the time scale is exactly the right one. We also study the similar probability for the exit from a neighborhood of the deterministic soliton solution. We are able to quantify the gain of eliminating the secular modes to better describe the persistence of the soliton.

We consider the problem of the error in soliton transmission in long-haul optical fibers caused by the spontaneous emission of noise inherent to amplification. We study two types of noises driving the stochastic focusing cubic one dimensional nonlinear Schr\"{o}dinger equation which appears in physics in that context. We focus on the fluctuations of the mass and arrival time or timing jitter. We give the small noise asymptotic of the tails of these two quantities for the two types of noises. We are then able to prove several results from physics among which the Gordon--Haus effect which states that the fluctuation of the arrival time is a much more limiting factor than the fluctuation of the mass. The physical results had been obtained with arguments difficult to fully justify mathematically., Comment: Published in at http://dx.doi.org/10.1214/07-AAP449 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We consider stochastic nonlinear Schrodinger equations driven by an additive noise. The noise is fractional in time with Hurst parameter H in (0,1). It is also colored in space and the space correlation operator is assumed to be nuclear. We study the local well-posedness of the equation. Under adequate assumptions on the initial data, the space correlations of the noise and for some saturated nonlinearities, we prove a sample path large deviations principle and a support result. These results are stated in a space of exploding paths which are Holder continuous in time until blow-up. We treat the case of Kerr nonlinearities when H > 1/2.

We consider weakly damped nonlinear Schr\"odinger equations perturbed by a noise of small amplitude. The small noise is either complex and of additive type or real and of multiplicative type. It is white in time and colored in space. Zero is an asymptotically stable equilibrium point of the deterministic equations. We study the exit from a neighborhood of zero, invariant by the flow of the deterministic equation, in $\xLtwo$ or in $\xHone$. Due to noise, large fluctuations off zero occur. Thus, on a sufficiently large time scale, exit from these domains of attraction occur. A formal characterization of the small noise asymptotic of both the first exit times and the exit points is given.

Uniform large deviations for the laws of the paths of the solutions of the stochastic nonlinear Schrodinger equation when the noise converges to zero are presented. The noise is a real multiplicative Gaussian noise. It is white in time and colored in space. The path space considered allows blow-up and is endowed with a topology analogue to a projective limit topology. Thus a large variety of large deviation principle may be deduced by contraction. As a consequence, asymptotics of the tails of the law of the blow-up time when the noise converges to zero are obtained.

Sample path large deviations for the laws of the solutions of stochastic nonlinear Schrodinger equations when the noise converges to zero are presented. The noise is a complex additive gaussian noise. It is white in time and colored space wise. The solutions may be global or blow-up in finite time, the two cases are distinguished. The results are stated in trajectory spaces endowed with projective limit topologies. In this setting, the support of the law of the solution is also characterized. As a consequence, results on the law of the blow-up time and asymptotics when the noise converges to zero are obtained. An application to the transmission of solitary waves in fiber optics is also given.