Linear representation of M-estimates in linear models

Consider the linear regression model, y_i = x_iβ₀ + e_i, i = l,…,n, and an M-estimate β of β_o obtained by minimizing Σρ(y_i — x_iβ), where ρ is a convex function. Let S_n = ΣX_iX_iX_i and r_n = S_n^½ (β — β₀) — S_n ² Σx_ih(e_i), where, with a suitable choice of h(.), the expression Σ x_ix(e,) provides a linear representation of β. Bahadur (1966) obtained the order of r_n as n→ ∞ when β_o is a one-dimensional location parameter representing the median, and Babu (1989) proved a similar result for the general regression parameter estimated by the LAD (least absolute deviations) method. We obtain the stochastic order of r_n as n → ∞ for a general M-estimate as defined above, which agrees with the results of Bahadur and Babu in the special cases considered by them.     

Pseudo‐empirical likelihood ratio confidence intervals for complex surveys

The authors show how an adjusted pseudo‐empirical likelihood ratio statistic that is asymptotically distributed as a chi‐square random variable can be used to construct confidence intervals for a finite population mean or a finite population distribution function from complex survey samples. They consider both non‐stratified and stratified sampling designs, with or without auxiliary information. They examine the behaviour of estimates of the mean and the distribution function at specific points using simulations calling on the Rao‐Sampford method of unequal probability sampling without replacement. They conclude that the pseudo‐empirical likelihood ratio confidence intervals are superior to those based on the normal approximation, whether in terms of coverage probability, tail error rates or average length of the intervals.     

Computational Comparison Studies of Quadratic Assignment Like Formulations for the In Silico Sequence Selection Problem in De Novo Protein Design

In this paper an O(n²) mathematical formulation for in silico sequence selection in de novo protein design proposed by Klepeis et al. (2003, 2004), in which the number of additional variables and linear constraints scales with the square of the number of binary variables, is compared to three O(n) formulations. It is found that the O(n²) formulation is superior to the O(n) formulations on most sequence search spaces. The superiority of the O(n²) formulation is due to the reformulation linearization techniques (RLTs), since the O(n²) formulation without RLTs is found to be computationally less efficient than the O(n) formulations. In addition, new algorithmic enhancing components of RLTs with inequality constraints, triangle inequalities, and Dead-End Elimination (DEE) type preprocessing are added to the O(n²) formulation. The current best O(n²) formulation, which is the original formulation from Klepeis et al. (2003, 2004) plus DEE type preprocessing, is proposed for in silico sequence search. For a test problem with a search space of 3.4×10⁴⁵ sequences, this new improved model is able to reduce the required CPU time by 67%.     

General definition and decomposition of projectors and some applications to statistical problems

A general definition of a set of projectors for decomposing a vector as the sum of vectors belonging to disjoint subspaces not necessarily spanning the whole space is given. Such projectors are defined only over the union of the disjoint subspaces. But their extension to the whole space is of some interest in statistical problems. Explicit expressions are obtained for projectors and their extensions in terms of matrices spanning the subspaces and g-inverses. Decomposition of a projector as the sum of projectors on subspaces is obtained and applied to problems arising in correlation analysis, analysis of variance and estimation of parameters in the Gauss-Markoff model.     

Lessons from the flipside: How do acquirers learn from divestitures to complete acquisitions?

We examine whether experience from prior divestitures is associated with higher completion likelihood in subsequent acquisitions. We argue that through divestitures, firms could extract meaningful knowledge that is helpful for completing their acquisitions, such as a general understanding of acquisition deal-making stages, and the stories and aims of the target in an M&A transaction. In addition, viewing divestitures as the flipside of acquisitions, we conjecture that the selling firm can observe how the divested component of their business is acquired, and vicariously learn from these observations. We also investigate the relative importance of learning from divestiture vis-à-vis acquisition experience in determining acquisition deal completion. Finally, we contend that the effect of learning from divestitures on acquisition deal completion depends on acquisition experience and deal value. We find evidence supporting our conjectures in a sample of 2164 M&A transactions from the worldwide computer and printing industries between 1991 and 2010.     

浅析网络财务的特点及其安全防范对策

时间、空间和效率构成了网络财务的三重特征,极大地提高了企业的工作效率和竞争力。但网络财务所依托的Internet/Intrannet体系,使用的是开放式的TCP/IP协议,它以广泛的形式进行传播,造成了搭截侦听、口令字试探和窃取身份假冒等安全问题的重大隐患。为此,企业必须建立科学严格的内部控制制度等安全防范对策。     

"全球地域化"语境下中国文学影响研究

在“全球地域化”语境下,中国文学要走向世界,就不能局限于单一文化的眼光,要积极开展跨文化的多维对话,在不同文化双向往来和互相认识中互补互动;中外文学史上有许多中国文学被“异”文化吸纳、接受的事例,总结这方面的历史经验,以史为“镜”,对推动中国文学在世界文化总体中的对话,有直接和积极的意义。