Bent函数估计的可计算达到上界

Bent函数的计数和数目估计问题与依据其设计的流密码的安全性有密切联系。通过将Bent函数表示为定序特征矩阵,引入Bent矩阵的概念;根据Bent函数的定义,得到Bent矩阵的一些性质;利用解决一阶相关免疫布尔函数计数问题的方法,给出Bent函数个数估计的一个基于整数分拆表示的可计算上界,计算实例说明该上界是可达到的上界。     

A class of partially linear single‐index survival models

The authors define a class of “partially linear single‐index” survival models that are more flexible than the classical proportional hazards regression models in their treatment of covariates. The latter enter the proposed model either via a parametric linear form or a nonparametric single‐index form. It is then possible to model both linear and functional effects of covariates on the logarithm of the hazard function and if necessary, to reduce the dimensionality of multiple covariates via the single‐index component. The partially linear hazards model and the single‐index hazards model are special cases of the proposed model. The authors develop a likelihood‐based inference to estimate the model components via an iterative algorithm. They establish an asymptotic distribution theory for the proposed estimators, examine their finite‐sample behaviour through simulation, and use a set of real data to illustrate their approach.     

科技翻译中英语屈折语素汉译的原则与策略探讨

依据现代翻译理论和从语言学的角度对现代英语屈折语素的句法功能与所表达的语法意义的分析,科技翻译中英语屈折语素汉译基本原则应是以意义为参照系;其汉译可采取隐译、显译和隐译、显译并举等办法。     

A lower bound for the transition density function of a stochastic differential equation

Let W_t be a one-dimensional Brownian motion on the probability space (Ω,F,P), and let dx_t = a(x_t)dt + b(x_t)dw_t, b²(x) > 0, be a one-dimensional Ito stochastic differential equation. For a(x) = a₀ + a₁x + … + a_nxⁿ on a bounded interval we obtain a lower bound for p(t,x,y), the transition density function of the homogeneous Markov process x_t, depending directly on the coefficients a₀,a₁, …, a_n, and b(x).     

Binary Response Models for Panel Data: Identification and Information

This paper considers a panel data model for predicting a binary outcome. The conditional probability of a positive response is obtained by evaluating a given distribution function (F) at a linear combination of the predictor variables. One of the predictor variables is unobserved. It is a random effect that varies across individuals but is constant over time. The semiparametric aspect is that the conditional distribution of the random effect, given the predictor variables, is unrestricted. This paper has two results. If the support of the observed predictor variables is bounded, then identification is possible only in the logistic case. Even if the support is unbounded, so that (from Manski (1987)) identification holds quite generally, the information bound is zero unless F is logistic. Hence consistent estimation at the standard pn rate is possible only in the logistic case.     

Generalized E(s 2) Criterion for Multilevel Supersaturated Designs

Several extensions of the popular E(s ²) criterion of Booth and Cox (1962 Booth , K. H. V. , Cox , D. R. ( 1962 ). Some systematic supersaturated designs . Technometrics 4 : 489 – 495 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) to multilevel supersaturated designs have been advanced in literature. These extensions are not unique due to different ways they measure overall nonorthogonality between all pairs of the columns of the model matrix. We exploit the connection of the E(s ²) criterion with A- and D-optimality that naturally lends itself to a generalized criterion for the multilevel situation in a unified way. The extensions provided in literature follow as special cases of the generalized criterion. A lower bound to the generalized criterion is derived for a wide class of designs, and a method of construction for the symmetrical case is discussed.     

Irreconcilability of P-value and Bayesian measure in two-sided hypothesis: Pareto distribution with the presence of nuisance parameter

Abstract

This article is concerned with the comparison of Bayesian and classical testing of a point null hypothesis for the Pareto distribution when there is a nuisance parameter. In the first stage, using a fixed prior distribution, the posterior probability is obtained and compared with the P-value. In the second case, lower bounds of the posterior probability of H₀, under a reasonable class of prior distributions, are compared with the P-value. It has been shown that even in the presence of nuisance parameters for the model, these two approaches can lead to different results in statistical inference.     

An upper bound on a ratio of variances

Several processes may be monitored in terms of their variances relative to each other by the maximum ratio of variances. Based on the observed value an upper confidence bound for the population value is derived and tables are supplied for its implementation. Such bounds may be used when the experimenter wishes to establish that the variances are "not too different". The usual testing of variances for equality is noticed to be inappropriate in such cases.