thinner lines represent finite element mesh, while the thicker ones represent the electric field distribution of the dominant mode.Firstly, given fixed fin thickness, the electrical field lines are apt to be focused in the dielectric area, and the density of the electrical field lines at the interface of the dielectric substrate and metallic fins increases as the value of the dielectric constant increases.Secondly, the dependence of fin thickness on the field pattern shows different configurations. For a smaller value of dielectric constant, the density of electrical field lines near the interface of dielectric substrate and metallic fins decreases as the fin thickness increases (for example, the case of r ϭ 2.22), while in the case of larger values of the dielectric constant, the field patterns almost remain unchanged as the fin thickness increases (for example, the case of r ϭ 10.0). CONCLUSIONSThe field patterns of the dominant mode in unilateral finline are presented by the hybrid finite-element method. The electrical field lines are mainly concentrated upon dielectric substrate. The influence of the fin thickness on the field pattern in unilateral finline can be ignored if the dielectric constant is larger enough. The figures in this paper have important values for the design of finline millimeter-wave integrated circuits.

In this article, we consider bootstrapping the Lasso estimator of the regression parameter in a multiple linear regression model. It is known that the standard bootstrap method fails to be consistent. Here, we propose a modified bootstrap method, and show that it provides valid approximation to the distribution of the Lasso estimator, for all possible values of the unknown regression parameter vector, including the case where some of the components are zero. Further, we establish consistency of the modified bootstrap method for estimating the asymptotic bias and variance of the Lasso estimator. We also show that the residual bootstrap can be used to consistently estimate the distribution and variance of the adaptive Lasso estimator. Using the former result, we formulate a novel data-based method for choosing the optimal penalizing parameter for the Lasso using the modified bootstrap. A numerical study is performed to investigate the finite sample performance of the modified bootstrap. The methodology proposed in the article is illustrated with a real data example.

Narrowing of the central sulcus, upward shift of the brain, and narrowing of CSF spaces at the vertex occurred frequently and predominantly in astronauts after long-duration flights. Further investigation, including repeated postflight imaging conducted after some time on Earth, is required to determine the duration and clinical significance of these changes. (Funded by the National Aeronautics and Space Administration.).

BackgroundThe efficacy of endovascular thrombectomy (ET) for acute ischemic stroke (AIS) in octogenarians is still controversial.ObjectiveTo evaluate, using a large multicenter cohort of patients, outcomes after ET in octogenarians compared with younger patients.MethodsData from prospectively maintained databases of patients undergoing ET for AIS at seven US-based comprehensive stroke centers between January 2013 and January 2018 were reviewed. Demographic, procedural, and outcome variables were collected. Outcomes included 90-day modified Rankin Scale (mRS) score, postprocedural National Institutes of Health Stroke Scale score, postprocedural hemorrhage, and mortality. Univariate and multivariate analyses were performed to assess the independent effect of age ≥80 on outcome measures. Subgroup analyses were also performed based on location of stroke, success of recanalization, or ET technique used.ResultsRates of functional independence (mRS score 0–2) after ET in elderly patients were significantly lower than for younger counterparts. Age ≥80 was independently associated with increased mortality and poor outcome. Age ≥80 showed an independent negative prognostic effect on outcome even when patients were divided according to thrombectomy technique, location of stroke, or success of recanalization. Age ≥80 independently predicted higher rate of postprocedural hemorrhage, but not success of recanalization. Baseline deficit and number of reperfusion attempts, but not Thrombolysis in Cerebral Infarction score were associated with lower odds of good outcome.ConclusionThe large effect size of ET on AIS outcomes is significantly diminished in the elderly population when using comparable selection criteria to those used in younger counterparts. This raises concerns about the risk–benefit ratio and the cost-effectiveness of performing this procedure in the elderly before optimizing patient selection.

Abstract. In this article, we derive the asymptotic distribution of the bootstrapped Lasso estimator of the regression parameter in a multiple linear regression model. It is shown that under some mild regularity conditions on the design vectors and the regularization parameter, the bootstrap approximation converges weakly to a random measure. The convergence result rigorously establishes a previously known heuristic formula for the limit distribution of the bootstrapped Lasso estimator. It is also shown that when one or more components of the regression parameter vector are zero, the bootstrap may fail to be consistent.

We discuss the choice of input parameters for the renormalization of the chargino and neutralino sector in the minimal supersymmetric standard model (MSSM) in the on-shell scheme. We show that one should chose the masses of a bino-like, a wino-like and a higgsino-like state as inputs in order to avoid large corrections to the masses of the other eigenstates in this sector. We also show that schemes where the higgsino-like input state is a neutralino are more stable than those where the mass of the higgsino-like chargino is used as input. The most stable scheme uses the masses of the wino-like chargino as well as the masses of the bino-and higgsino-like neutralinos as inputs. * Authors' names are listed in alphabetical order † On leave from Institute of Nuclear Physics, PAN, Kraków, ul. Radzikowskiego 152, Poland.

the Adaptive LASSO (ALASSO) method for simultaneous variable selection and estimation of the regression parameters, and established its oracle property. In this paper, we investigate the rate of convergence of the ALASSO estimator to the oracle distribution when the dimension of the regression parameters may grow to infinity with the sample size. It is shown that the rate critically depends on the choices of the penalty parameter and the initial estimator, among other factors, and that confidence intervals (CIs) based on the oracle limit law often have poor coverage accuracy. As an alternative, we consider the residual bootstrap method for the ALASSO estimators that has been recently shown to be consistent; cf. Chatterjee and Lahiri [J. Amer. Statist. Assoc. 106 (2011a) 608-625]. We show that the bootstrap applied to a suitable studentized version of the ALASSO estimator achieves second-order correctness, even when the dimension of the regression parameters is unbounded. Results from a moderately large simulation study show marked improvement in coverage accuracy for the bootstrap CIs over the oracle based CIs.

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