Структурная стабильность многослойных Zr/Nb систем при облучении ионами Не+ в широком диапазоне доз

В работе исследована структурная стабильность многослойных покрытий Zr/Nb с различной толщиной индивидуального слоя при облучении ионами He?. Многослойные Zr/Nb покрытия с толщиной индивидуального слоя 10 - 100 нм были подвергнуты облучению ионами гелия с флюенсом 1•10?? - 1•10?? ион/см? с энергией 25 кэВ. По результатам проведенного рентгеноструктурного анализа и измерения электрического сопротивления было выявлено, что при больших дозах облучения покрытия испытывают реориентацию с изменением микро- и макронапряжений. В работе была была определена оптимальная толщина индивидуального слоя, обеспечивающая высокую структурную стабильность системы в целом, которая составила 25 нм.The structural stability of Zr/Nb multilayer coatings with different individual layer thicknesses under irradiation with He? ions was studied. Multilayer Zr/Nb coatings with an individual layer thickness of 10–100 nm were irradiated with 25 keV helium ions with a fluence of 1 • 10?? - 1 • 10?? ion / cm? . According to the results of X-ray diffraction analysis and measurement of electrical resistance, it was found that at high doses of irradiation, coatings undergo reorientation with a change in micro- and macrostresses. In the work, the optimal thickness of the individual layer was determined, which ensures high structural stability of the system as a whole, which was 25 nm

Прогноз резервуаров в магматических породах доюрского возраста на примере Александровского мегавала (Томская область)

In a multi-sample experiment, we model the parameters of an equal load-sharing system by means of link functions in sequential order statistics models, and then discuss the estimation of these parameters based on a given link function. Different link functions are examined along with the corresponding maximum likelihood estimators, and their properties are studied both analytically and through Monte Carlo simulations

When to wait for more evidence?: real options analysis in proton therapy.

Purpose. Trends suggest that cancer spending growth will accelerate. One method for controlling costs is to examine whether the benefits of new technologies are worth the extra costs. However, especially new and emerging technologies are often more costly, while limited clinical evidence of superiority is available. In that situation it is often unclear whether to adopt the new technology now, with the risk of investing in a suboptimal therapy, or to wait for more evidence, with the risk of withholding patients their optimal treatment. This trade-off is especially difficult when it is costly to reverse the decision to adopt a technology, as is the case for proton therapy. Real options analysis, a technique originating from financial economics, assists in making this trade-off. Methods. We examined whether to adopt proton therapy, as compared to stereotactic body radiotherapy, in the treatment of inoperable stage I non-small cell lung cancer. Three options are available: adopt without further research; adopt and undertake a trial; or delay adoption and undertake a trial. The decision depends on the expected net gain of each option, calculated by subtracting its total costs from its expected benefits. Results. In The Netherlands, adopt and trial was found to be the preferred option, with an optimal sample size of 200 patients. Increase of treatment costs abroad and costs of reversal altered the preferred option. Conclusion. We have shown that real options analysis provides a transparent method of weighing the costs and benefits of adopting and/or further researching new and expensive technologies. The Oncologist 2011;16:1752-176

Comments on "Hybrid censoring: models, inferential results and applications" by N. Balakrishnan and D. Kundu

A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. In this review, we first discuss Type-I and Type-II hybrid censoring schemes and associated inferential issues. Next, we present details on developments regarding generalized hybrid censoring and unified hybrid censoring schemes that have been introduced in the literature. Hybrid censoring schemes have been adopted in competing risks set-up and in step-stress modeling and these results are outlined next. Recently, two new censoring schemes, viz., progressive hybrid censoring and adaptive progressive censoring schemes have been introduced in the literature. We discuss these censoring schemes and describe inferential methods based on them, and point out their advantages and disadvantages. Determining an optimal hybrid censoring scheme is an important design problem, and we shed some light on this issue as well. Finally, we present some examples to illustrate some of the results described here. Throughout the article, we mention some open problems and suggest some possible future work for the benefit of readers interested in this area of research

Deriving the asymptotic distribution of U- and V-statistics of dependent data using weighted empirical processes

It is commonly acknowledged that V-functionals with an unbounded kernel are not Hadamard differentiable and that therefore the asymptotic distribution of U- and V-statistics with an unbounded kernel cannot be derived by the Functional Delta Method (FDM). However, in this article we show that V-functionals are quasi-Hadamard differentiable and that therefore a modified version of the FDM (introduced recently in (J. Multivariate Anal. 101 (2010) 2452-2463)) can be applied to this problem. The modified FDM requires weak convergence of a weighted version of the underlying empirical process. The latter is not problematic since there exist several results on weighted empirical processes in the literature; see, for example, (J. Econometrics 130 (2006) 307-335, Ann. Probab. 24 (1996) 2098-2127, Empirical Processes with Applications to Statistics (1986) Wiley, Statist. Sinica 18 (2008) 313-333). The modified FDM approach has the advantage that it is very flexible w.r.t. both the underlying data and the estimator of the unknown distribution function. Both will be demonstrated by various examples. In particular, we will show that our FDM approach covers mainly all the results known in literature for the asymptotia distribution of U- and V-statistics based on dependent data - and our assumptions are by tendency even weaker. Moreover, using our FDM approach we extend these results to dependence concepts that are not covered by the existing literature

Using linear interpolation to reduce the order of the coverage error of nonparametric prediction intervals based on right-censored data

We prove a general result showing that a simple linear interpolation between adjacent random variables reduces the coverage error of nonparametric prediction intervals for a future observation from the same underlying distribution function from O(n−1)O(n−1) to O(n−2)O(n−2). To illustrate the result we show that it can be applied to various scenarios of right censored data including Type-II censored samples, pooled Type-II censored data, and progressively Type-II censored order statistics. We further illustrate the result by simulations indicating that the desired level of significance is almost attained for moderate sample sizes

Identical distributions of single variates and random corvex combinations of uniform fractional order statistics.

It is shown that any uniform fractional order statistic can be represented in distribution by a random convex combination of any number of neighboring uniform fractional order statistics with weights being products of independent beta distributed random variables. In particular, the result is valid for ordinary order statistics

Estimators based on data-drive generalized weighted Cramer-vaon Mises Distances under censoring - with applications to mixture models

Estimators based on data-driven generalized weighted cramér-von mises distances are defined for data that are subject to a possible right censorship. The function used to measure the distance between the data, summarized by the kaplan–meier estimator, and the target model is allowed to depend on the sample size and, for example, on the number of censored items. It is shown that the estimators are consistent and asymptotically multivariate normal for every p dimensional parametric family fulfiling some mild regularity conditions. The results are applied to finite mixtures. Simulation results for finite mixtures indicate that the estimators are useful for moderate sample sizes. Furthermore, the simulation results reveal the usefulness of sample size dependent and censoring sensitive distance functions for moderate sample sizes. Moreover, the estimators for the mixing proportion seem to be fairly robust against a ‘symmetric’ contamination model even when censoring is present

A modified functional delta method and its application to the estimation of risk measures

AbstractThe classical functional delta method (FDM) provides a convenient tool for deriving the asymptotic distribution of statistical functionals from the weak convergence of the respective empirical processes. However, for many interesting functionals depending on the tails of the underlying distribution this FDM cannot be applied since the method typically relies on Hadamard differentiability w.r.t. the uniform sup-norm. In this article, we present a version of the FDM which is suitable also for nonuniform sup-norms, with the outcome that the range of application of the FDM enlarges essentially. On one hand, our FDM, which we shall call the modified FDM, works for functionals that are “differentiable” in a weaker sense than Hadamard differentiability. On the other hand, it requires weak convergence of the empirical process w.r.t. a nonuniform sup-norm. The latter is not problematic since there exist strong respective results on weighted empirical processes obtained by Shorack and Wellner (1986) [25], Shao and Yu (1996) [23], Wu (2008) [32], and others. We illustrate the gain of the modified FDM by deriving the asymptotic distribution of plug-in estimates of popular risk measures that cannot be treated with the classical FDM