NON-SYMMETRICAL CORRESPONDENCE ANALYSIS WITH CONCATENATION AND LINEAR CONSTRAINTS

Correspondence analysis is a popular statistical technique used to identify graphically the presence, and structure, of association between two or more cross-classified categorical variables. Such a procedure is very useful when it is known that there is a symmetric (two-way) relationship between the variables. When such a relationship is known not to exist, non-symmetrical correspondence analysis is more appropriate as a method of establishing the source of association. This paper highlights some tools that can be used to explore the behaviour of asymmetric categorical variables. These tools consist of confidence regions, the link between non-symmetrical correspondence analysis and the analysis of variance of categorical variables, and the effect of imposing linear constraints. We also explore the application of non-symmetrical correspondence analysis to three-way contingency tables.     

高频磁结构与磁极限关系的研究与现状

动态磁谱参数,包括微波磁导率实部和虚部、截止频率等,与材料本征磁参数、结构形貌参数的制约关系,作为高频磁性材料性能的标杆和发展方向的理论指导,一直伴随着高频磁性材料的发展而不断演变,是动态磁化物理理论研究和磁性功能材料设计所关注的核心问题。磁结构决定了极限关系式的形式,而极限关系式为磁结构的设计、应用和发展提供了理论指导。该文综合介绍了近年来国内外高频磁结构和磁极限关系研究的发展情况,着重分析两者相辅相成的关系和研究方法,并归纳了该领域的发展趋势和存在的一些主要问题。     

Empirical Likelihood Inference for the Cox Model with Time-dependent Coefficients via Local Partial Likelihood

Abstract.  The Cox model with time-dependent coefficients has been studied by a number of authors recently. In this paper, we develop empirical likelihood (EL) pointwise confidence regions for the time-dependent regression coefficients via local partial likelihood smoothing. The EL simultaneous confidence bands for a linear combination of the coefficients are also derived based on the strong approximation methods. The EL ratio is formulated through the local partial log-likelihood for the regression coefficient functions. Our numerical studies indicate that the EL pointwise/simultaneous confidence regions/bands have satisfactory finite sample performances. Compared with the confidence regions derived directly based on the asymptotic normal distribution of the local constant estimator, the EL confidence regions are overall tighter and can better capture the curvature of the underlying regression coefficient functions. Two data sets, the gastric cancer data and the Mayo Clinic primary biliary cirrhosis data, are analysed using the proposed method.     

Confidence regions for two proportions from independent negative binomial distributions

The negative binomial distribution offers an alternative view to the binomial distribution for modeling count data. This alternative view is particularly useful when the probability of success is very small, because, unlike the fixed sampling scheme of the binomial distribution, the inverse sampling approach allows one to collect enough data in order to adequately estimate the proportion of success. However, despite work that has been done on the joint estimation of two binomial proportions from independent samples, there is little, if any, similar work for negative binomial proportions. In this paper, we construct and investigate three confidence regions for two negative binomial proportions based on three statistics: the Wald (W), score (S) and likelihood ratio (LR) statistics. For large-to-moderate sample sizes, this paper finds that all three regions have good coverage properties, with comparable average areas for large sample sizes but with the S method producing the smaller regions for moderate sample sizes. In the small sample case, the LR method has good coverage properties, but often at the expense of comparatively larger areas. Finally, we apply these three regions to some real data for the joint estimation of liver damage rates in patients taking one of two drugs.     

Nonparametric estimation of varying-coefficient single-index models

The varying-coefficient single-index model has two distinguishing features: partially linear varying-coefficient functions and a single-index structure. This paper proposes a nonparametric method based on smoothing splines for estimating varying-coefficient functions and an unknown link function. Moreover, the average derivative estimation method is applied to obtain the single-index parameter estimates. For interval inference, Bayesian confidence intervals were obtained based on Bayes models for varying-coefficient functions and the link function. The performance of the proposed method is examined both through simulations and by applying it to Boston housing data.     

Simultaneous one-sided tolerance intervals for sets of contrasts from normal populations

This paper considers the problem of constructing simultaneous prediction and tolerance intervals for sets of contrasts of normal variables in situations where simultaneous intervals are available. Tables are given with critical values used in simultaneous tolerance bounds for two classes of contrasts: pairwise many-one and profile type.     

Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk

Abstract. We give a rigorous study of weak convergence of the wild bootstrap for non‐parametric estimation of the cumulative event probability of a competing risk. The data may be subject to independent left‐truncation and right‐censoring. Inclusion of left‐truncation is motivated by a study on pregnancy outcomes. The wild bootstrap includes as one case a popular resampling technique, where the limit distribution is approximated by repeatedly generating standard normal variates, while the data are kept fixed. Simulation results and a data example are also presented.     

Confidence Intervals in Regression That Utilize Uncertain Prior Information About a Vector Parameter

Consider a linear regression model with independent normally distributed errors. Suppose that the scalar parameter of interest is a specified linear combination of the components of the regression parameter vector. Also suppose that we have uncertain prior information that a parameter vector, consisting of specified distinct linear combinations of these components, takes a given value. Part of our evaluation of a frequentist confidence interval for the parameter of interest is the scaled expected length, defined to be the expected length of this confidence interval divided by the expected length of the standard confidence interval for this parameter, with the same confidence coefficient. We say that a confidence interval for the parameter of interest utilizes this uncertain prior information if (a) the scaled expected length of this interval is substantially less than one when the prior information is correct, (b) the maximum value of the scaled expected length is not too large and (c) this confidence interval reverts to the standard confidence interval, with the same confidence coefficient, when the data happen to strongly contradict the prior information. We present a new confidence interval for a scalar parameter of interest, with specified confidence coefficient, that utilizes this uncertain prior information. A factorial experiment with one replicate is used to illustrate the application of this new confidence interval.     

论属性决策系统及其在中小企业业绩评价中的应用

根据属性集和属性测度理论,属性综合决策系统可分为三个子系统:第一个为单指标性能函数分析子系统,第二个为多指标综合性能函数分析子系统;第三个为识别分析子系统.将属性测度决策系统应用于中小企业业绩评价,并经实例分析证明该系统是合理、实用和有效的.     

英语教学中学生自信心的培养及实效性

该文从心理学角度简析了在英语学习中产生自信心不足的内、外原因及对英语教学的负效应。主要讨论了在英语教学过程中 ,学生自信心的培养及在语言应用中的实效性。同时 ,也对英语学生自信心的培养和心理调控提出了一些建议