基于排序集样本和辅助变量中位数的均值比率估计方法

排序集抽样下利用辅助变量中位数构建了总体均值的改进比率估计模型,分析了该比率估计量的偏差和均方误差,并与简单随机抽样下的比率估计比较,证明了改进后的比率估计均方误差更小。以农作物播种面积和产量为研究对象进行实例分析,研究表明,基于排序集样本和辅助变量中位数的比率估计方法可以有效提高估计精度,验证了该构造方法的可行性。     

Some notes on Church's thesis and the theory of games

This paper considers games in normal form played by Turing Machines. The machines are fed as input all the relevent information and then are required to play the game. Some impossibility results are derived for this set-up. In particular, it is shown that no Turing Machine exists which will always play the correct strategy given its opponent's choice. Such a result also generalizes to the case in which attention is restricted to economically optimizing machines only. The paper also develops a model of knowledge. This allows the main results of the paper to be interpreted as stemming out of the impossibility of always deciding whether a player is rational or not in some appropriate sense.     

Analyzing the Productive and Protective Dimensions of Welfare: Looking Beyond the OECD

Several theorists have argued that social policy in East Asia can be seen as representing a distinctive welfare ideal type based around ‘productive welfare’. However, we have contested such claims in earlier work (Hudson and Kühner 2009) and, in common with theorists such as Castells, have suggested that some of the welfare states of the Organisation for Economic Co‐operation and Development (OECD) have a distinct bias towards the ‘productive’ rather than ‘protective’ dimensions of welfare. In this article, we build on our earlier work, utilizing fuzzy set ideal type analysis (FSITA) to explore the balance between ‘productive' and ‘protective’ dimensions of welfare state activity. Here we extend our analysis beyond the OECD, incorporating a range of nations on the ‘fringe’ of the OECD from Latin America, East Asia and the non‐OECD parts of Europe. In so doing, we contest simple notions of welfare regimes aligning with regional blocks. Primarily, however, we highlight the advantages of the ‘diversity‐orientated’ approach to data analysis that fuzzy set methods facilitate in comparison with standard quantitative techniques. In particular, we utilize FSITA to avoid data availability and reliability issues that have plagued quantitatively informed classifications of global welfare regimes. Not least, we argue FSITA allows for the contextualization of cases in a way that is sealed to quantitatively driven, comparative research. Thus, we argue FSITA has an important role to play in attempts to extend the inclusiveness of the ‘welfare modelling business’ in a manner that reflects diverse and highly significant cases beyond the Western lens that dominates the literature.     

Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects

In this paper, a model is developed for the selection of personnel for a multiple phase project which takes into account the match between the skills possessed by each individual, the skills needed for each phase, and rather flexible budget considerations. The algorithm uses the fuzzy construct of compatibility to measure the fit of a person’s skill set to the goal set for each project phase. Based on the individual fuzzy compatibility measures, the team is formed from combined levels of compatibility and acceptable levels of quality defined for the goal set. 1 and 2 present the background information necessary to an understanding of project management phases and compatibility of skills. The development of the model and subsequent algorithm in 3 and 4, respectively rely on fuzzy measures of compatibility. Finally, an application is presented in Section 4 with conclusions stated in Section 5.     

An Approximation Algorithm for Alignment of Multiple Sequences using Motif Discovery

Given a set of N sequence, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. The quality of the alignment is usually measured by penalizing the mis-matches and gaps, and rewarding the matches with appropriate weight functions. However for larger values of N, additional constraints are required to give meaningful alignments. We identify a user-controlled parameter, an alignment number K (2 K N): this additional requirement constrains the alignment to have at least K sequences agree on a character, whenever possible, in the alignment. We identify a natural optimization problem for this approach called the K-MSA problem. We show that the problem is MAX SNP hard. We give a natural extension of this problem that incorporates biological relevance by using motifs (common patterns in the sequences) and give an approximation algorithm for this problem in terms of the motifs in the data. MUSCA is an implementation of this approach and our experimental results indicate that this approach is efficient, particularly on large numbers of long sequences, and gives good alignments when tested on biological data such as DNA and protein sequences.     

从数据库中发现知识的方法研究与应用

知识发现与数据开来是90年代中期兴起的决策支持新技术．本文以近年来获得快速发展的粗集方法为基础,从数据库中自动抽取相关属性,发现知识,提出了最小属性集算法以及规则化简算法,从而改进粗集方法使之得到实用的规则,并应用于一个疾病诊断．     

一类非线性二阶微分系统两点边值问题

给出了一类非线性二阶微分系统两点边值共振问题可解的一组充分条件.     

某些赋范线性空间中非线性联合逼近的α阶强唯一性

本文给出了满足某些条件的赋范线性空间上联合逼近的α阶强唯一性定理,利用这些定理获得了:若G是L_P(H~(K.P))中的联合太阳集(或弱拟凸集,如果g_o是G中对F={f_i}∈σ联合最佳逼近,则M>O,存在C_P>O,使得g∈G∩B(g_o,M)有sum from i=1 to ∞γ_i‖f_i-g‖~p≥sum from i=1 to ∞γ_i‖f_i-g_o‖~p C_p‖g-g_o‖~α。其中α=max(2,P)