Graphical Comparison of Covariance Matrices

Procedures for comparing within-group covariance matrices are developed, based on separate analyses of the variances and of the correlations. The variances and the correlations are represented as two two-way tables, with the columns representing groups. Graphical procedures based on comparisons of linear regressions are presented, by considering a multiplicative columns-regression model for the interaction of groups x variances and of groups x correlations. A multivariate comparison leads to the use of a generalized eigenanalysis to display any differences in covariance structure.     

On complete classes of experiments for certain invariant problems of linear inference

For certain non-singularly estimable full-rank appropriately invariant problems of linear inference in the setting of block designs, some complete classes of experiments have been characterized through the relation between the relevant C-matrices and their g-inverses (of the Moore-Penrose type) in regard to the specific invariance criterion discussed here. It follows that for a $\text{G}$-invariant non-singularly estimable full-rank problem, a complete class of experiments formally consists only of $\text{G}$-invariant designs.     

Nearly optimal covariate designs—Part II

This is a continuation to Part I toward our efforts for providing illustrative examples in the context of analysis of covariance (ANCOVA) models and related analyses of data. We discuss four more examples here, and these are derived from standard textbooks. We re-visit these examples with a view to suggest optimal/nearly optimal designs for estimation of the covariate parameter(s).     

Alternative GMM estimators for first-order autoregressive panel model: An improving efficiency approach

This article considers first-order autoregressive panel model that is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix that has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. This led to a loss of efficiency. Therefore, we present new GMM estimators based on optimal or suboptimal weighting matrices. Monte Carlo study indicates that the bias and efficiency of the new estimators are more reliable than the conventional estimators.     

Stochastic monotonicity and comparability of Markov chains with block-monotone transition matrices and their applications to queueing systems

ABSTRACT

Motivated by various applications in queueing theory, this article is devoted to the stochastic monotonicity and comparability of Markov chains with block-monotone transition matrices. First, we introduce the notion of block-increasing convex order for probability vectors, and characterize the block-monotone matrices in the sense of the block-increasing order and block-increasing convex order. Second, we characterize the Markov chain with general transition matrix by martingale and provide a stochastic comparison of two block-monotone Markov chains under the two block-monotone orders. Third, the stochastic comparison results for the Markov chains corresponding to the discrete-time GI/G/1 queue with different service distributions under the two block-monotone orders are given, and the lower bound and upper bound of the Markov chain corresponding to the discrete-time GI/G/1 queue in the sense of the block-increasing convex order are found.     

Optimum covariate designs in split-plot and strip-plot design set-ups

The problem considered is that of finding optimum covariate designs for estimation of covariate parameters in standard split-plot and strip-plot design set-ups with the levels of the whole-plot factor in r randomised blocks. Also an extended version of a mixed orthogonal array has been introduced, which is used to construct such optimum covariate designs. Hadamard matrices, as usual, play the key role for such construction.     

基于转移概率矩阵的企业债券定价模型

企业债券评估的主要方法为结构化风险模型和密度式风险模型,但中国企业债券市场评级数据少、缺乏历史违约事件,因此可以通过对JLT模型进行改进,以加权平均的方式计算经验转移概率矩阵,然后利用市场上各评级债券的时间序列数据计算风险中性违约概率,并通过市场宏观数据判断经济处于上升期还是衰退期,据此计算条件转移概率矩阵,最后通过债券远期折现的方式构建适合中国市场特色的债券定价模型。实证部分选取了2014年部分新发行债券对模型进行了实证检验,并对国内企业债券定价的现状进行了分析。     

基于功能和零部件配置元的在线定制顾客需求研究

根据公理化设计理论,研究产品从功能域向物理域的映射,讨论功能与部件之间的关系,提出功能配置元和零部件配置元的概念,定义顾客基于功能配置元和零部件配置元的产品在线定制矩阵;通过对顾客在线调查结果的分析,得到顾客对功能配置元和零部件配置元及其特征项总体关注程度,定义在线定制服务提供商在线定制综合能力评价指标体系,构建反映在线定制服务提供商在线定制综合能力系数的计算方法,结合顾客对功能配置元和零部件配置元及其特征项总体关注程度的分析方法,确定在线定制服务提供商提供给顾客进行产品在线定制的各功能配置元和零部件配置元及其特征项,并对顾客在线定制产品的类型进行了划分.最后举例证明该方法的可行性.