C239. The continued fraction for lnr( z): a stieljes conjecture

Estimation in logistic-normal models for correlated and overdispersed binomial data is complicated by the numerical evaluation of often intractable likelihood functions. Penalized quasilikelihood (PQL) estimators of fixed effects and variance components are known to be seriously biased for binary data. A simple correction procedure has been proposed to improve the performance of the PQL estimators. The proposed method is illustrated by analyzing infectious disease data. Its performance is compared, by means of simulations, with that of the Bayes approach using the Gibbs sampler.     

Testing for mean and variance differences with samples from distributions that may be non-normal with unequal variances

The problem of comparing several samples to decide whether the means and/or variances are significantly different is considered. It is shown that with very non-normal distributions even a very robust test to compare the means has poor properties when the distributions have different variances, and therefore a new testing scheme is proposed. This starts by using an exact randomization test for any significant difference (in means or variances) between the samples. If a non-significant result is obtained then testing stops. Otherwise, an approximate randomization test for mean differences (but allowing for variance differences) is carried out, together with a bootstrap procedure to assess whether this test is reliable. A randomization version of Levene's test is also carried out for differences in variation between samples. The five possible conclusions are then that (i) there is no evidence of any differences, (ii) evidence for mean differences only, (iii) evidence for variance differences only, (iv) evidence for mean and variance differences, or (v) evidence for some indeterminate differences. A simulation experiment to assess the properties of the proposed scheme is described. From this it is concluded that the scheme is useful as a robust, conservative method for comparing samples in cases where they may be from very non-normal distributions.     

Establishing safety stocks for master production schedules

The basic master production scheduling problem assumes that periodic demands are known with certainty. Uncertainty in the forecasts arc typically accommodated afterwards by adding safety stocks to a schedule. Two popular methods for establishing safety stocks are: (1) the constant cycle service level method; and (2) the constant safety stock method. This paper outlines these methods and then develops a third method which results in optimal safety stocks. The paper includes an experimental investigation aimed at comparing performances of the three safety stock methods. The constant safety stock method is shown to perform within one or two per cent of optimal, while the constant cycle service level method performs worse under most conditions. Shorter lead times, variable order interval lengths, and time-dependent forecast errors all adversely affect the performances of the non-optimal methods. An operations manager could use these results to evaluate the appropriateness of the methods for his master production scheduling environment.     

区域经济社会发展水平的因子分析模糊综合评价

区域经济社会发展水平评价实践中，通常采用的方法除不能兼顾主客观性外，还忽略了评价指标之间的相关性，针对这一问题，提出了一种在因子分析基础上结合模糊评价的新方法，并将其用于湘潭市经济社会发展水平综合评价。     

俄罗斯低生育率的成因及“母亲资本法”的成效评价

近年来俄罗斯人口数量的急剧减少与人口低生育率有直接联系,经济因素、社会生育观、宗教以及价值观差异、个人家庭状况都影响出生率动态,"母亲资本法"的出台对人口出生率的促进作用是毋庸置疑的,但这些社会家庭政策同时也存在迄待完善的地方。     

高校图书馆读者满意度调查研究

图书馆服务是以满足读者需求为目的的服务，衡量图书馆服务质量的根本标准就是读者满意程度。从高校图书馆读者满意度的特定涵义出发,根据图书馆资源的利用现状，分析了读者满意度的影响因素。最后，对提高图书馆服务质量提出了建议。     

Learning to learn and productivity growth: Evidence from a new car-assembly plant

This paper models learning by experience beyond the experience curve, including the possibility of “learning to learn”: the pace of learning increases over time by building on what has already been learned. We compare the extended deterministic learning model with Jovanovic and Nyarkos' [26] stochastic learning. The theoretical models are tested with data on the total factor productivity of a car-assembly plant in its first months of operation. We find that the deterministic “mixed learning model”, where the speed of learning is equal to a constant plus a learning to learn effect, is the one that best fits the empirical data. The mixed learning model results in a time pattern of total factor productivity growth, first increasing and later decreasing, different from the always decreasing rate of growth of the learning curve, opening new perspectives on the study of learning by experience.     

Efficiency decomposition in network data envelopment analysis with slacks-based measures

Conventional data envelopment analysis (DEA) treats the production system as a black box when measuring efficiency, ignoring its internal structure. By taking the operations of the component processes of the system into consideration, several network DEA models have been developed. Of these, the slacks-based measure (SBM) approach has attracted much attention for its ability to provide suitable efficiency measures, especially for weakly efficient production units. This paper proposes a general SBM model for network systems, and is able to decompose the system efficiency into a weighted average of the process efficiencies. This relationship holds for all types of network structure. An example shows that the network model has stronger discriminating power than the conventional black-box model, and the system efficiency is indeed a weighted average of the process efficiencies. The decomposition of the system efficiency helps identify key factors to improve the performance of a production unit.     

基于因子分析模型的基金业绩分析

近年来,我国证券投资基金规模不断壮大,成为我国资本市场上一支不可或缺的力量.证券投资基金是众多中小投资者的重要理财工具,因此,其业绩评估成为众多投资者以及管理者关心的重要话题.文章以成立于2008年之前的开放式股票型基金,共61只,为研究对象,分析了影响基金整体业绩的基金月平均收益率、标准差、夏普指数等若干主要因素.通过运用数据,采用因子分析的方法进而对基金业绩进行分析,综合得出了影响基金业绩的三个综合影响因子,并通过因子得分分析得出整体业绩排名前十的基金.为众多投资者提供了一个基金业绩评估的有效方法.